Systems and Methods for Device Localization

ABSTRACT

Systems and methods for localizing portable devices are illustrated. One embodiment includes a method for locating a portable device in a network that includes several reference devices. The method measures characteristics of signals transmitted via signal paths between reference devices and a portable device, normalizes the measurements to estimate characteristics of the signal paths, and estimates the likelihood that the portable device is in a particular location. Systems and methods for training prediction models include a method that includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device based on the context data using a prediction model, identifying a target stationary device from the several stationary devices, training the prediction model based on based on the predicted stationary device and the received input.

CROSS-REFERENCE TO RELATED APPLICATIONS

The current application is a continuation of U.S. patent applicationSer. No. 16/672,271 entitled “Systems and Methods for DeviceLocalization” filed Nov. 1, 2019, which claims the benefit of andpriority under 35 U.S.C. § 119(e) to U.S. Provisional Patent ApplicationNo. 62/907,367 entitled “Systems and Methods for Device Localization andPrediction” filed Sep. 27, 2019, the disclosures of which are herebyincorporated by reference in their entireties for all purposes.

TECHNICAL FIELD

The present technology relates to consumer goods and, more particularly,to methods, systems, products, features, services, and other elementsdirected to localization and/or target device prediction in mediaplayback systems or some aspect thereof.

BACKGROUND

Options for accessing and listening to digital audio in an out-loudsetting were limited until in 2003, when SONOS, Inc. filed for one ofits first patent applications, entitled “Method for Synchronizing AudioPlayback between Multiple Networked Devices,” and began offering a mediaplayback system for sale in 2005. The SONOS Wireless HiFi System enablespeople to experience music from many sources via one or more networkedplayback devices. Through a software control application installed on asmartphone, tablet, or computer, one can play what he or she wants inany room that has a networked playback device. Additionally, using acontroller, for example, different songs can be streamed to each roomthat has a playback device, rooms can be grouped together forsynchronous playback, or the same song can be heard in all roomssynchronously.

Given the ever-growing interest in digital media, there continues to bea need to develop consumer-accessible technologies to further enhancethe listening experience.

SUMMARY

Systems and methods for localizing portable devices are illustrated. Oneembodiment includes a method for locating a portable device in a networkthat includes several reference devices. The method includes steps formeasuring characteristics of signals transmitted via signal pathsbetween each of several reference devices over a period of time,measuring characteristics of signals transmitted via signal pathsbetween a portable device and each of the several reference devices,normalizing the measurements to estimate characteristics of the signalpaths between each of the several reference devices and between theportable device and each of the reference devices, and estimating thelikelihood that the portable device is in a particular location usingthe estimated characteristics of the signal paths between each of theseveral reference devices and the estimated characteristics of thesignal paths between the portable device and each of the severalreference devices.

In a further embodiment, at least some of the several reference devicesinclude different transmitter implementations.

In still another embodiment, the portable device has a transmitterimplementation that differs from the transmitter implementations of atleast one of the reference devices.

In a still further embodiment, the measured characteristics of aparticular signal includes at least one of a received signal strengthindicator (RSSI) value, an identifier for a sending reference devicethat transmitted the particular signal, and a timestamp for theparticular signal.

In yet another embodiment, estimating the likelihood includes computinga set of probabilities that the portable device is near each of at leastone reference device of the several reference devices.

In a yet further embodiment, normalizing the measurements for a firstreference device comprises calculating a first signal-strength ratio ofsignals received at the first reference device from the portable deviceto signals received at the first reference device from a secondreference device, and calculating a second signal-strength ratio ofsignals received at the second reference device from the portable deviceto signals received at the second reference device from the firstreference device, wherein computing the set of probabilities for thefirst reference device includes computing a ratio of the firstsignal-strength ratio to the second signal-strength ratio.

In another additional embodiment, normalizing the measurements for afirst reference device comprises calculating a first signal-strengthratio of signals received at the first reference device from theportable device to signals received at the first reference device from asecond reference device, and calculating a second signal-strength ratioof signals received at a third reference device from the portable deviceto signals received at the third reference device from the secondreference device, wherein computing the set of probabilities for thefirst reference device includes computing a ratio of the firstsignal-strength ratio to the second signal-strength ratio.

In a further additional embodiment, computing the set of probabilitiescomprises identifying an offset based on a difference in RSSI values forthe first signal path and the second signal path, and determining anormalized set of one or more RSSI values for the first and secondsignal paths based on the identified offset.

In another embodiment again, normalizing the measurements to estimatecharacteristics of a particular signal path between a first referencedevice and a second reference device includes computing a weightedaverage of at least one characteristic for a first set of one or moresignals from the first reference device to the second reference deviceand a second set of one or more signals from the second reference deviceto the first reference device.

In a further embodiment again, the weighted average is weighted based ontimestamps associated with the first and second sets of signals.

In still yet another embodiment, the method further includes steps forestimating which of the reference devices is closest to the portabledevice based on the estimated likelihood.

In a still yet further embodiment, the method further includes steps forselecting a computing reference device of the several reference devicesfor performing the steps of normalizing the measurements and estimatingthe likelihood.

In still another additional embodiment, selecting the computingreference device includes identifying an idle reference device of theseveral reference devices.

In a still further additional embodiment, selecting the computingreference device includes identifying an infrequently-used referencedevice of the several reference devices.

In still another embodiment again, the method further includes steps foridentifying a nearest reference device from the set of reference devicesbased on the estimated likelihood, and transferring audio playing at theportable device to play at the nearest reference device.

In a still further embodiment again, the method further includes stepsfor determining a change in location based on changes in the estimatedlikelihood over a duration of time.

One embodiment includes a method for locating a portable device in amedia playback system includes several reference devices. The methodincludes steps for measuring characteristics of signals transmitted viasignal paths between each of several reference devices over a period oftime, wherein the several reference devices includes several referenceplayback devices. The method further includes steps for measuringcharacteristics of signals transmitted via signal paths between aportable device and each of the several reference devices. The methodincludes steps for normalizing the measurements to estimatecharacteristics of the signal paths between each of the severalreference devices and between the portable device and each of thereference devices. The method also includes steps for estimating thelikelihood that the portable device is in a particular location usingthe estimated characteristics of the signal paths between each of theseveral reference devices and the estimated characteristics of thesignal paths between the portable device and each of the severalreference devices. The method includes steps for identifying a targetreference playback device of the several reference devices based on theestimated likelihood.

In a further embodiment, at least some of the several reference devicesinclude different transmitter implementations.

In still another embodiment, the portable device has a transmitterimplementation that differs from the transmitter implementations of atleast one of the reference devices.

In a still further embodiment, the measured characteristics of aparticular signal includes at least one of a received signal strengthindicator (RSSI) value, an identifier for a sending reference devicethat transmitted the particular signal, and a timestamp for theparticular signal.

In yet another embodiment, estimating the likelihood includes computinga set of probabilities that the portable device is near each of at leastone reference device of the several reference devices.

In a yet further embodiment, normalizing the measurements for a firstreference device comprises calculating a first signal-strength ratio ofsignals received at the first reference device from the portable deviceto signals received at the first reference device from a secondreference device, and calculating a second signal-strength ratio ofsignals received at the second reference device from the portable deviceto signals received at the second reference device from the firstreference device, wherein computing the set of probabilities for thefirst reference device includes computing a ratio of the firstsignal-strength ratio to the second signal-strength ratio.

In another additional embodiment, normalizing the measurements for afirst reference device comprises calculating a first signal-strengthratio of signals received at the first reference device from theportable device to signals received at the first reference device from asecond reference device, and calculating a second signal-strength ratioof signals received at a third reference device from the portable deviceto signals received at the third reference device from the secondreference device, wherein computing the set of probabilities for thefirst reference device includes computing a ratio of the firstsignal-strength ratio to the second signal-strength ratio.

In a further additional embodiment, computing the set of probabilitiescomprises identifying an offset based on a difference in RSSI values forthe first signal path and the second signal path, and determining anormalized set of one or more RSSI values for the first and secondsignal paths based on the identified offset.

In another embodiment again, normalizing the measurements to estimatecharacteristics of a particular signal path between a first referencedevice and a second reference device includes computing a weightedaverage of at least one characteristic for a first set of one or moresignals from the first reference device to the second reference deviceand a second set of one or more signals from the second reference deviceto the first reference device.

In a further embodiment again, the weighted average is weighted based ontimestamps associated with the first and second sets of signals.

In still yet another embodiment, the method further includes steps forestimating which of the reference devices is closest to the portabledevice based on the estimated likelihood.

In a still yet further embodiment, the method further includes steps forselecting a computing reference device of the several reference devicesfor performing the steps of normalizing the measurements and estimatingthe likelihood.

In still another additional embodiment, selecting the computingreference device includes identifying an idle reference device of theseveral reference devices.

In a still further additional embodiment, selecting the computingreference device includes identifying an infrequently-used referencedevice of the several reference devices.

In still another embodiment again, the method further includes steps foridentifying a nearest reference device from the set of reference devicesbased on the estimated likelihood, and transferring audio playing at theportable device to play at the nearest reference device.

In a still further embodiment again, the method further includes stepsfor determining a change in location based on changes in the estimatedlikelihood over a duration of time.

In yet another additional embodiment, the several reference devicesfurther includes a set of one or more controller devices for controllingplayback devices in the media playback system.

One embodiment includes a playback device including one or moreamplifiers configured to drive one or more speakers, one or moreprocessors, and data storage having stored therein instructionsexecutable by the one or more processors to cause the playback device toperform a method. The method includes steps for obtainingcharacteristics of signals transmitted via signal paths between each ofseveral reference playback devices in a media playback system over aperiod of time, obtaining characteristics of signals transmitted viasignal paths between a portable device and each of the several referenceplayback devices, and normalizing the measurements to estimatecharacteristics of the signal paths between each of the severalreference devices and between the portable device and each of thereference devices. The method further includes steps for estimating thelikelihood that the portable device is in a particular location usingthe estimated characteristics of the signal paths between each of theseveral reference devices and the estimated characteristics of thesignal paths between the portable device and each of the severalreference devices, identifying a set of one or more target referenceplayback devices of the several reference devices based on the estimatedlikelihood, and transmitting the set of target reference playbackdevices to the portable device. The portable device modifies a userinterface at the portable device based on the ranked listing.

In a yet further additional embodiment, the playback device is one ofthe several reference playback devices.

One embodiment includes a controller device that includes a displayconfigured to display a graphical user interface, one or moreprocessors, and data storage having stored therein instructionsexecutable by the one or more processors to cause the controller deviceto perform a method. The method includes steps for obtainingcharacteristics of signals transmitted via signal paths between each ofseveral reference playback devices in the media playback system over aperiod of time, obtaining characteristics of signals transmitted viasignal paths between the controller device and each of the severalreference playback devices, and normalizing the measurements to estimatecharacteristics of the signal paths between each of the severalreference devices and between the portable device and each of thereference devices. The process further includes steps for estimating thelikelihood that the portable device is in a particular location usingthe estimated characteristics of the signal paths between each of theseveral reference devices and the estimated characteristics of thesignal paths between the portable device and each of the severalreference devices, identifying a set of one or more target referenceplayback devices of the several reference devices based on the estimatedlikelihood, and modifying the graphical user interface displayed on thedisplay based on the identified set of target reference playbackdevices.

One embodiment includes a method for locating a portable device in amedia playback system includes several reference devices. The methodincludes steps for receiving characteristics of signals transmitted viasignal paths between each of several reference devices over a period oftime, wherein the several reference devices includes several referenceplayback devices, receiving characteristics of signals transmitted viasignal paths between a portable device and each of the several referencedevices, and normalizing the measurements to estimate characteristics ofthe signal paths between each of the several reference devices andbetween the portable device and each of the reference devices. Themethod further includes steps for estimating the likelihood that theportable device is in a particular location using the estimatedcharacteristics of the signal paths between each of the severalreference devices and the estimated characteristics of the signal pathsbetween the portable device and each of the several reference devices,and identifying a set of one or more target reference playback devicesof the several reference devices based on the estimated likelihood.

In yet another embodiment again, the method is performed by the portabledevice.

In a yet further embodiment again, the method further includes steps formodifying a user interface at the portable device based on theidentified set of target reference playback devices.

In another additional embodiment again, modifying the user interfaceincludes at least one of the group of highlighting the set of targetreference playback devices and reordering a listing of referenceplayback devices.

In a further additional embodiment again, the method is performed by areference device of the several reference devices.

In still yet another additional embodiment, the method further includessteps for transmitting the set of target reference playback devices tothe portable device, wherein the portable device modifies a userinterface at the portable device based on the ranked listing.

In a further embodiment, transmitting the set of target referencedevices includes transmitting a ranked list of the set of targetreference playback devices, wherein the portable device modifies adisplay order of the target reference playback devices based on theranked list.

In still another embodiment, the portable device modifies the userinterface to emphasize at least one target reference playback device ofthe set of target reference playback devices.

Systems and methods for training prediction models are illustrated. Oneembodiment includes a method for training a prediction model in anetwork. The method includes steps for receiving context data for aportable device in a system, wherein the context data includeslocalization data that describes a location of the portable device,identifying a predicted stationary device from several stationarydevices that is predicted based on the context data using a predictionmodel, receiving input identifying a target stationary device from theseveral stationary devices, generating training data based on thepredicted stationary device and the received input, updating theprediction model based on the generated training data.

In yet another additional embodiment, the localization data includes amatrix of probabilities.

In a yet further additional embodiment, selecting the predictedstationary device comprises providing a set of inputs to the predictionmodel, the set of inputs includes the received localization data, andgenerating probabilities for each stationary device of the severalstationary devices using the prediction model.

In yet another embodiment again, selecting the predicted stationarydevice further includes selecting the stationary device with a highestprobability.

In a yet further embodiment again, selecting the predicted stationarydevice further includes transmitting a control signal to the predictedstationary device.

In another additional embodiment again, selecting the predictedstationary device further comprises ranking at least a subset of theseveral stationary devices based on the generated probabilities, andproviding an ordered listing of the several stationary devices to theportable device based on the ranking, wherein the portable devicepresents the ordered listing in a user interface of the portable device.

In a further additional embodiment again, receiving input identifying atarget stationary device includes receiving a control command from thetarget stationary device.

In still yet another additional embodiment, receiving input identifyinga target stationary device includes receiving a selection of the targetstationary device from the portable device.

In a further embodiment, generating the training data includes storingthe context data as a training data sample and an identification of thetarget stationary device as a label for the training data sample.

In still another embodiment, updating the prediction model comprisespassing the training data sample through the prediction model togenerate probabilities for each stationary device of the severalstationary devices, computing a loss based on the generatedprobabilities and the target stationary device, and updating weights ofthe prediction model based on the computed loss.

In a still further embodiment, generating the training data furtherincludes storing the predicted stationary device, wherein updating theprediction model comprises computing a loss based on the predictedstationary device and the target stationary device, and updating weightsof the prediction model based on the computed loss.

In yet another embodiment, the context data further includes at leastone of a time of day, day of the week, and a user identity.

One embodiment includes a method for training a prediction engine. Themethod includes steps for monitoring a system includes several devices,determining whether to capture training data, identifying a true userinteraction, identifying a predicted user interaction from a predictionmodel, and generating training data based on the true user interactionand the predicted user interaction.

In a yet further embodiment, monitoring the system includes localizing aportable device within a system to determine a probability matrix,wherein the probability matrix indicates likelihoods that the portabledevice is nearest to each of the several devices, wherein identifyingthe predicted user interaction includes feeding the probability matrixas an input to the prediction model.

In another additional embodiment, the method further includes steps forgathering context data, wherein the context data includes a set of datafrom the group consisting of user data, system state data, andlocalization data.

In a further additional embodiment, monitoring the system includeslocalizing a portable device in the system, and determining whether tocapture training data includes determining whether a confidence levelfor the localizing of the portable device exceeds a threshold value.

One embodiment includes a method for training a prediction model in amedia playback system. The method includes steps for receiving contextdata for a portable device in the media playback system, wherein thecontext data includes localization data that describes a location of theportable device, identifying a predicted stationary playback device fromseveral stationary playback devices that is predicted based on thecontext data using a prediction model, and receiving input identifying atarget stationary playback device from the several stationary playbackdevices. The method further includes steps for generating training databased on the predicted stationary playback device and the receivedinput, and updating the prediction model based on the generated trainingdata.

In a further embodiment, the localization data includes a matrix ofprobabilities.

In still another embodiment, selecting the predicted stationary playbackdevice comprises providing a set of inputs to the prediction model, theset of inputs includes the received localization data, and generatingprobabilities for each stationary playback device of the severalstationary playback devices using the prediction model.

In a still further embodiment, selecting the predicted stationaryplayback device further includes selecting the stationary playbackdevice with a highest probability.

In yet another embodiment, selecting the predicted stationary playbackdevice further includes transmitting a control signal to the predictedstationary playback device.

In a yet further embodiment, selecting the predicted stationary playbackdevice further comprises ranking at least a subset of the severalstationary playback devices based on the generated probabilities, andproviding an ordered listing of the several stationary playback devicesto the portable device based on the ranking, wherein the portable devicepresents the ordered listing in a user interface of the portable device.

In another additional embodiment, receiving input identifying a targetstationary playback device includes receiving a control command from thetarget stationary playback device.

In a further additional embodiment, receiving input identifying a targetstationary playback device includes receiving a selection of the targetstationary playback device from the portable device.

In another embodiment again, generating the training data includesstoring the context data as a training data sample and an identificationof the target stationary playback device as a label for the trainingdata sample.

In a further embodiment again, updating the prediction model comprisespassing the training data sample through the prediction model togenerate probabilities for each stationary playback device of theseveral stationary playback devices, computing a loss based on thegenerated probabilities and the target stationary playback device, andupdating weights of the prediction model based on the computed loss.

In still yet another embodiment, generating the training data furtherincludes storing the predicted stationary playback device, whereinupdating the prediction model comprises computing a loss based on thepredicted stationary playback device and the target stationary playbackdevice, and updating weights of the prediction model based on thecomputed loss.

In a still yet further embodiment, the context data further includes atleast one of a time of day, day of the week, and a user identity.

One embodiment includes a method for training a prediction engine in amedia playback system. The method includes steps for monitoring a mediaplayback system that includes several playback devices, determiningwhether to capture training data, identifying a true user interaction,identifying a predicted user interaction from a prediction model, andgenerating training data based on the true user interaction and thepredicted user interaction.

In still another additional embodiment, monitoring the system includeslocalizing a portable device within the media playback system todetermine a probability matrix, wherein the probability matrix indicateslikelihoods that the portable device is nearest to each of the severalplayback devices, wherein identifying the predicted user interactionincludes feeding the probability matrix as an input to the predictionmodel.

In a still further additional embodiment, the method further includessteps for obtaining context data, wherein the context data includes aset of data from the group consisting of user data, system state data,and localization data.

In still another embodiment again, monitoring the media playback systemincludes localizing a portable device in the media playback system, anddetermining whether to capture training data includes determiningwhether a confidence level for the localizing of the portable deviceexceeds a threshold value.

One embodiment includes a portable device comprising one or moreprocessors and data storage having stored therein instructionsexecutable by the one or more processors to cause the portable device toperform a method. The method includes steps for receiving context datafor the portable device, wherein the context data includes localizationdata that describes a location of the portable device, identifying apredicted stationary playback device from several stationary playbackdevices in a media playback system that is predicted based on thecontext data using a prediction model, and receiving input identifying atarget stationary playback device from the several stationary playbackdevices. The method further includes steps for generating training databased on the predicted stationary playback device and the receivedinput, and updating the prediction model based on the generated trainingdata.

In a still further embodiment again, the method further includes stepsfor a touch-screen display configured to display a graphical userinterface.

In yet another additional embodiment, the method further includesupdating the graphical user interface displayed on the touch-screendisplay based on the predicted stationary playback device.

In a yet further additional embodiment, receiving input identifying thetarget stationary playback device includes receiving input identifyingthe target stationary playback device via the graphical user interfacedisplayed on the touch-screen display.

Additional embodiments and features are set forth in part in thedescription that follows, and in part will become apparent to thoseskilled in the art upon examination of the specification or may belearned by the practice of the technology described herein. A furtherunderstanding of the nature and advantages of the technology describedherein may be realized by reference to the remaining portions of thespecification and the drawings, which forms a part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Features, aspects, and advantages of the presently disclosed technologymay be better understood with regard to the following description,appended claims, and accompanying drawings.

FIG. 1A is a partial cutaway view of an environment having a mediaplayback system configured in accordance with aspects of the disclosedtechnology.

FIG. 1B is a schematic diagram of the media playback system of FIG. 1Aand one or more networks.

FIG. 2A is a functional block diagram of an example playback device.

FIG. 2B is an isometric diagram of an example housing of the playbackdevice of FIG. 2A.

FIGS. 3A-3E are diagrams showing example playback device configurationsin accordance with aspects of the disclosure.

FIG. 4A is a functional block diagram of an example controller device inaccordance with aspects of the disclosure.

FIGS. 4B and 4C are controller interfaces in accordance with aspects ofthe disclosure.

FIG. 5 is a functional block diagram of certain components of an examplenetwork microphone device in accordance with aspects of the disclosure.

FIG. 6A is a diagram of an example voice input.

FIG. 6B is a graph depicting an example sound specimen in accordancewith aspects of the disclosure.

FIG. 7 conceptually illustrates an example of a process for localizing aportable device in a networked sensor system in accordance with anembodiment.

FIG. 8 illustrates an example of a data structure for characteristics ofsignals in a networked sensor system in accordance with an embodiment.

FIG. 9 illustrates an example of differences in signals based on signaldirection between two devices in accordance with an embodiment.

FIG. 10 illustrates an example of changing probabilities for portabledevice moving between reference devices in accordance with anembodiment.

FIG. 11 illustrates an example of localizing a portable device in anetworked sensor system in accordance with an embodiment.

FIG. 12 illustrates an example of a localization element that localizesportable devices in accordance with an embodiment.

FIG. 13 illustrates an example of a localization application inaccordance with an embodiment.

FIG. 14 conceptually illustrates an example of a process for training aprediction model in accordance with an embodiment.

FIG. 15 illustrates an example of a prediction element that trains andpredicts target devices in accordance with an embodiment.

FIG. 16 illustrates an example of a prediction application in accordancewith an embodiment.

FIGS. 17A-B illustrate examples of a graphical user interface (GUI)based on predicted target devices.

The drawings are for purposes of illustrating example embodiments, butit should be understood that these embodiments are not limited to thearrangements and instrumentality shown in the drawings. In the drawings,identical reference numbers identify at least generally similarelements. To facilitate the discussion of any particular element, themost significant digit or digits of any reference number refers to theFigure in which that element is first introduced. For example, element103 a is first introduced and discussed with reference to FIG. 1A.

DETAILED DESCRIPTION I. Overview

As devices become smarter, it is becoming increasingly desirable tolocate and interact with devices within indoor areas (e.g., a home).However, due to the variety of different floorplans and variousinterfering effects found indoors (e.g., walls, appliances, furniture,etc.), it can be difficult to locate a device in such an environment.While some conventional solutions have used wireless signals to locate adevice based on signal strength of a wireless signal from a single knownsource (e.g., a BLUETOOTH beacon, WI-FI transmitter, etc.) detected bythe device, such solutions can often be inaccurate or inconsistent,varying based on the transmitter/receiver hardware in the device and/orthe BLUETOOTH beacon. For example, a first device may appear to becloser to the BLUETOOTH beacon than a second device because the firstdevice detected a wireless signal transmitted by the BLUETOOTH beacon ata higher power level than the second device. However, the first devicemay simply employ a high gain antenna and, in fact, be significantlyfurther away from the BLUETOOTH beacon than the second device. Further,some conventional systems require specific calibration for anenvironment, which can be difficult and tedious, particularly as theposition of devices in the system change. In contrast to theseconventional methods, some embodiments of the technology describedherein may be employed to advantageously measure and normalize signalsbetween a portable device and reference devices (e.g., speakers, NMDs,controllers, etc.) in a system (e.g., a media playback system (MPS)) toestimate, for each reference device, a likelihood that the portabledevice is located near the reference device. As a result, locationsidentified in accordance with the techniques described herein can beconsiderably more accurate, without the need for calibration oradjustments for different environments, relative to locations identifiedusing conventional approaches.

In some cases, it can be desirable for the interaction of devices to beautomated or streamlined based on the location of a portable device(e.g., a mobile phone). It can also be difficult to identify a desiredtarget device to interact with based on the location of the portabledevice, not only because the location of the portable device can bedifficult to determine, but also because the location of the portabledevice alone may not provide sufficient context to correctly predict thedesired target device. For example, a user may always turn on thekitchen SONOS speakers first thing in the morning from their bedroom,even when they have a closer set of speakers in their bedroom, becausethey intend to go to the kitchen. Conventional solutions use simplerules-based heuristics based on manual programming by a user, but suchsolutions can be difficult to maintain and are often unable to adjust toa user's changing preferences. In contrast to these conventionalmethods, some embodiments of the technology described herein may beemployed to advantageously generate training data based on a user'spatterns in order to train a target device prediction model specific toa user (or household). Such processes can result in more accuratepredictions with less manual user input. In particular, the processesmay enable a system to identify repeated behaviors in user behavior andadapt to the identified user behavior to enhance subsequent userinteractions with the system.

Although many of the examples described herein refer to MPSs, oneskilled in the art will recognize that similar systems and methods canbe used in a variety of different systems to locate, predict targetdevices, and/or train such a predictor, including (but not limited to)security systems, Internet of Things (IoT) systems, etc., withoutdeparting from the scope of the present disclosure. Further, thetechniques described herein may be advantageously employed for devicelocalization in any of a variety of operating environments includingindoor environments, outdoor environments, and mixed indoor-outdoorenvironments.

While some embodiments described herein may refer to functions performedby given actors, such as “users” and/or other entities, it should beunderstood that this description is for purposes of explanation only.The claims should not be interpreted to require action by any suchexample actor unless explicitly required by the language of the claimsthemselves.

II. Example Operating Environment

FIGS. 1A and 1B illustrate an example configuration of a media playbacksystem 100 (or “MPS 100”) in which one or more embodiments disclosedherein may be implemented. Referring first to FIG. 1A, the MPS 100 asshown is associated with an example home environment having a pluralityof rooms and spaces, which may be collectively referred to as a “homeenvironment,” “smart home,” or “environment 101.” The environment 101comprises a household having several rooms, spaces, and/or playbackzones, including a master bathroom 101 a, a master bedroom 101 b(referred to herein as “Nick's Room”), a second bedroom 101 c, a familyroom or den 101 d, an office 101 e, a living room 101 f, a dining room101 g, a kitchen 101 h, and an outdoor patio 101 i. While certainembodiments and examples are described below in the context of a homeenvironment, the technologies described herein may be implemented inother types of environments. In some embodiments, for example, the MPS100 can be implemented in one or more commercial settings (e.g., arestaurant, mall, airport, hotel, a retail or other store), one or morevehicles (e.g., a sports utility vehicle, bus, car, a ship, a boat, anairplane), multiple environments (e.g., a combination of home andvehicle environments), and/or another suitable environment wheremulti-zone audio may be desirable.

Within these rooms and spaces, the MPS 100 includes one or morecomputing devices. Referring to FIGS. 1A and 1B together, such computingdevices can include playback devices 102 (identified individually asplayback devices 102 a-102 o), network microphone devices 103(identified individually as “NMDs” 103 a-102 i), and controller devices104 a and 104 b (collectively “controller devices 104”). Referring toFIG. 1B, the home environment may include additional and/or othercomputing devices, including local network devices, such as one or moresmart illumination devices 108 (FIG. 1B), a smart thermostat 110, and alocal computing device 105 (FIG. 1A). In embodiments described below,one or more of the various playback devices 102 may be configured asportable playback devices, while others may be configured as stationaryplayback devices. For example, the headphones 102 o (FIG. 1B) are aportable playback device, while the playback device 102 d on thebookcase may be a stationary device. As another example, the playbackdevice 102 c on the Patio may be a battery-powered device, which mayallow it to be transported to various areas within the environment 101,and outside of the environment 101, when it is not plugged in to a walloutlet or the like. Localization, prediction, and/or training ofprediction models in accordance with a number of embodiments can beperformed on such computing devices.

With reference still to FIG. 1B, the various playback, networkmicrophone, and controller devices 102-104 and/or other network devicesof the MPS 100 may be coupled to one another via point-to-pointconnections and/or over other connections, which may be wired and/orwireless, via a LAN 111 including a network router 109. For example, theplayback device 102 j in the Den 101 d (FIG. 1A), which may bedesignated as the “Left” device, may have a point-to-point connectionwith the playback device 102 a, which is also in the Den 101 d and maybe designated as the “Right” device. In a related embodiment, the Leftplayback device 102 j may communicate with other network devices, suchas the playback device 102 b, which may be designated as the “Front”device, via a point-to-point connection and/or other connections via theLAN 111.

As further shown in FIG. 1B, the MPS 100 may be coupled to one or moreremote computing devices 106 via a wide area network (“WAN”) 107. Insome embodiments, each remote computing device 106 may take the form ofone or more cloud servers. The remote computing devices 106 may beconfigured to interact with computing devices in the environment 101 invarious ways. For example, the remote computing devices 106 may beconfigured to facilitate streaming and/or controlling playback of mediacontent, such as audio, in the home environment 101.

In some implementations, the various playback devices, NMDs, and/orcontroller devices 102-104 may be communicatively coupled to at leastone remote computing device associated with a voice activated system(“VAS”) and at least one remote computing device associated with a mediacontent service (“MCS”). For instance, in the illustrated example ofFIG. 1B, remote computing devices 106 a are associated with a VAS 190and remote computing devices 106 b are associated with an MCS 192.Although only a single VAS 190 and a single MCS 192 are shown in theexample of FIG. 1B for purposes of clarity, the MPS 100 may be coupledto multiple, different VASes and/or MCSes. In some implementations,VASes may be operated by one or more of AMAZON, GOOGLE, APPLE,MICROSOFT, SONOS or other voice assistant providers. In someimplementations, MCSes may be operated by one or more of SPOTIFY,PANDORA, AMAZON MUSIC, or other media content services.

As further shown in FIG. 1B, the remote computing devices 106 furtherinclude remote computing device 106 c configured to perform certainoperations, such as remotely facilitating media playback functions,managing device and system status information, directing communicationsbetween the devices of the MPS 100 and one or multiple VASes and/orMCSes, among other operations. In one example, the remote computingdevices 106 c provide cloud servers for one or more SONOS Wireless HiFiSystems. Remote computing devices can be used for parts of localization,prediction, and/or training of prediction models in accordance with anumber of embodiments.

In various implementations, one or more of the playback devices 102 maytake the form of or include an on-board (e.g., integrated) networkmicrophone device. For example, the playback devices 102 a-e include orare otherwise equipped with corresponding NMDs 103 a-e, respectively. Aplayback device that includes or is equipped with an NMD may be referredto herein interchangeably as a playback device or an NMD unlessindicated otherwise in the description. In some cases, one or more ofthe NMDs 103 may be a stand-alone device. For example, the NMDs 103 fand 103 g may be stand-alone devices. A stand-alone NMD may omitcomponents and/or functionality that is typically included in a playbackdevice, such as a speaker or related electronics. For instance, in suchcases, a stand-alone NMD may not produce audio output or may producelimited audio output (e.g., relatively low-quality audio output).

The various playback and network microphone devices 102 and 103 of theMPS 100 may each be associated with a unique name, which may be assignedto the respective devices by a user, such as during setup of one or moreof these devices. For instance, as shown in the illustrated example ofFIG. 1B, a user may assign the name “Bookcase” to playback device 102 dbecause it is physically situated on a bookcase. Similarly, the NMD 103f may be assigned the named “Island” because it is physically situatedon an island countertop in the Kitchen 101 h (FIG. 1A). Some playbackdevices may be assigned names according to a zone or room, such as theplayback devices 102 e, 1021, 102 m, and 102 n, which are named“Bedroom,” “Dining Room,” “Living Room,” and “Office,” respectively.Further, certain playback devices may have functionally descriptivenames. For example, the playback devices 102 a and 102 b are assignedthe names “Right” and “Front,” respectively, because these two devicesare configured to provide specific audio channels during media playbackin the zone of the Den 101 d (FIG. 1A). The playback device 102 c in thePatio may be named portable because it is battery-powered and/or readilytransportable to different areas of the environment 101. Other namingconventions are possible.

As discussed above, an NMD may detect and process sound from itsenvironment, such as sound that includes background noise mixed withspeech spoken by a person in the NMD's vicinity. For example, as soundsare detected by the NMD in the environment, the NMD may process thedetected sound to determine if the sound includes speech that containsvoice input intended for the NMD and ultimately a particular VAS. Forexample, the NMD may identify whether speech includes a wake wordassociated with a particular VAS.

In the illustrated example of FIG. 1B, the NMDs 103 are configured tointeract with the VAS 190 over a network via the LAN 111 and the router109. Interactions with the VAS 190 may be initiated, for example, whenan NMD identifies in the detected sound a potential wake word. Theidentification causes a wake-word event, which in turn causes the NMD tobegin transmitting detected-sound data to the VAS 190. In someimplementations, the various local network devices 102-105 (FIG. 1A)and/or remote computing devices 106 c of the MPS 100 may exchangevarious feedback, information, instructions, and/or related data withthe remote computing devices associated with the selected VAS. Suchexchanges may be related to or independent of transmitted messagescontaining voice inputs. In some embodiments, the remote computingdevice(s) and the media playback system 100 may exchange data viacommunication paths as described herein and/or using a metadata exchangechannel as described in U.S. Application Publication No.US-2017-0242653, and titled “Voice Control of a Media Playback System,”which is herein incorporated by reference in its entirety.

Upon receiving the stream of sound data, the VAS 190 determines if thereis voice input in the streamed data from the NMD, and if so the VAS 190will also determine an underlying intent in the voice input. The VAS 190may next transmit a response back to the MPS 100, which can includetransmitting the response directly to the NMD that caused the wake-wordevent. The response is typically based on the intent that the VAS 190determined was present in the voice input. As an example, in response tothe VAS 190 receiving a voice input with an utterance to “Play Hey Judeby The Beatles,” the VAS 190 may determine that the underlying intent ofthe voice input is to initiate playback and further determine thatintent of the voice input is to play the particular song “Hey Jude.”After these determinations, the VAS 190 may transmit a command to aparticular MCS 192 to retrieve content (i.e., the song “Hey Jude”), andthat MCS 192, in turn, provides (e.g., streams) this content directly tothe MPS 100 or indirectly via the VAS 190. In some implementations, theVAS 190 may transmit to the MPS 100 a command that causes the MPS 100itself to retrieve the content from the MCS 192.

In certain implementations, NMDs may facilitate arbitration amongst oneanother when voice input is identified in speech detected by two or moreNMDs located within proximity of one another. For example, theNMD-equipped playback device 102 d in the environment 101 (FIG. 1A) isin relatively close proximity to the NMD-equipped Living Room playbackdevice 102 m, and both devices 102 d and 102 m may at least sometimesdetect the same sound. In such cases, this may require arbitration as towhich device is ultimately responsible for providing detected-sound datato the remote VAS. Examples of arbitrating between NMDs may be found,for example, in previously referenced U.S. Application Publication No.US-2017-0242653.

In certain implementations, an NMD may be assigned to, or otherwiseassociated with, a designated or default playback device that may notinclude an NMD. For example, the Island NMD 103 f in the Kitchen 101 h(FIG. 1A) may be assigned to the Dining Room playback device 102 l,which is in relatively close proximity to the Island NMD 103 f. Inpractice, an NMD may direct an assigned playback device to play audio inresponse to a remote VAS receiving a voice input from the NMD to playthe audio, which the NMD might have sent to the VAS in response to auser speaking a command to play a certain song, album, playlist, etc.Additional details regarding assigning NMDs and playback devices asdesignated or default devices may be found, for example, in previouslyreferenced U.S. Application Publication No. US-2017-0242653.

Further aspects relating to the different components of the example MPS100 and how the different components may interact to provide a user witha media experience may be found in the following sections. Whilediscussions herein may generally refer to the example MPS 100,technologies described herein are not limited to applications within,among other things, the home environment described above. For instance,the technologies described herein may be useful in other homeenvironment configurations comprising more or fewer of any of theplayback, network microphone, and/or controller devices 102-104. Forexample, the technologies herein may be utilized within an environmenthaving a single playback device 102 and/or a single NMD 103. In someexamples of such cases, the LAN 111 (FIG. 1B) may be eliminated and thesingle playback device 102 and/or the single NMD 103 may communicatedirectly with the remote computing devices 106 a-d. In some embodiments,a telecommunication network (e.g., an LTE network, a 5G network, etc.)may communicate with the various playback, network microphone, and/orcontroller devices 102-104 independent of a LAN.

While specific implementations of MPS's have been described above withrespect to FIGS. 1A and 1B, there are numerous configurations of MPS's,including, but not limited to, those that do not interact with remoteservices, systems that do not include controllers, and/or any otherconfiguration as appropriate to the requirements of a given application.

a. Example Playback & Network Microphone Devices

FIG. 2A is a functional block diagram illustrating certain aspects ofone of the playback devices 102 of the MPS 100 of FIGS. 1A and 1B. Asshown, the playback device 102 includes various components, each ofwhich is discussed in further detail below, and the various componentsof the playback device 102 may be operably coupled to one another via asystem bus, communication network, or some other connection mechanism.In the illustrated example of FIG. 2A, the playback device 102 may bereferred to as an “NMD-equipped” playback device because it includescomponents that support the functionality of an NMD, such as one of theNMDs 103 shown in FIG. 1A.

As shown, the playback device 102 includes at least one processor 212,which may be a clock-driven computing component configured to processinput data according to instructions stored in memory 213. The memory213 may be a tangible, non-transitory, computer-readable mediumconfigured to store instructions that are executable by the processor212. For example, the memory 213 may be data storage that can be loadedwith software code 214 that is executable by the processor 212 toachieve certain functions.

In one example, these functions may involve the playback device 102retrieving audio data from an audio source, which may be anotherplayback device. In another example, the functions may involve theplayback device 102 sending audio data, detected-sound data (e.g.,corresponding to a voice input), and/or other information to anotherdevice on a network via at least one network interface 224. In yetanother example, the functions may involve the playback device 102causing one or more other playback devices to synchronously playbackaudio with the playback device 102. In yet a further example, thefunctions may involve the playback device 102 facilitating being pairedor otherwise bonded with one or more other playback devices to create amulti-channel audio environment. Numerous other example functions arepossible, some of which are discussed below.

As just mentioned, certain functions may involve the playback device 102synchronizing playback of audio content with one or more other playbackdevices. During synchronous playback, a listener may not perceivetime-delay differences between playback of the audio content by thesynchronized playback devices. U.S. Pat. No. 8,234,395 filed on Apr. 4,2004, and titled “System and method for synchronizing operations among aplurality of independently clocked digital data processing devices,”which is hereby incorporated by reference in its entirety, provides inmore detail some examples for audio playback synchronization amongplayback devices.

To facilitate audio playback, the playback device 102 includes audioprocessing components 216 that are generally configured to process audioprior to the playback device 102 rendering the audio. In this respect,the audio processing components 216 may include one or moredigital-to-analog converters (“DAC”), one or more audio preprocessingcomponents, one or more audio enhancement components, one or moredigital signal processors (“DSPs”), and so on. In some implementations,one or more of the audio processing components 216 may be a subcomponentof the processor 212. In operation, the audio processing components 216receive analog and/or digital audio and process and/or otherwiseintentionally alter the audio to produce audio signals for playback.

The produced audio signals may then be provided to one or more audioamplifiers 217 for amplification and playback through one or morespeakers 218 operably coupled to the amplifiers 217. The audioamplifiers 217 may include components configured to amplify audiosignals to a level for driving one or more of the speakers 218.

Each of the speakers 218 may include an individual transducer (e.g., a“driver”) or the speakers 218 may include a complete speaker systeminvolving an enclosure with one or more drivers. A particular driver ofa speaker 218 may include, for example, a subwoofer (e.g., for lowfrequencies), a mid-range driver (e.g., for middle frequencies), and/ora tweeter (e.g., for high frequencies). In some cases, a transducer maybe driven by an individual corresponding audio amplifier of the audioamplifiers 217. In some implementations, a playback device may notinclude the speakers 218, but instead may include a speaker interfacefor connecting the playback device to external speakers. In certainembodiments, a playback device may include neither the speakers 218 northe audio amplifiers 217, but instead may include an audio interface(not shown) for connecting the playback device to an external audioamplifier or audio-visual receiver.

In addition to producing audio signals for playback by the playbackdevice 102, the audio processing components 216 may be configured toprocess audio to be sent to one or more other playback devices, via thenetwork interface 224, for playback. In example scenarios, audio contentto be processed and/or played back by the playback device 102 may bereceived from an external source, such as via an audio line-in interface(e.g., an auto-detecting 3.5 mm audio line-in connection) of theplayback device 102 (not shown) or via the network interface 224, asdescribed below.

As shown, the at least one network interface 224, may take the form ofone or more wireless interfaces 225 and/or one or more wired interfaces226. A wireless interface may provide network interface functions forthe playback device 102 to wirelessly communicate with other devices(e.g., other playback device(s), NMD(s), and/or controller device(s)) inaccordance with a communication protocol (e.g., any wireless standardincluding IEEE 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.15, 4Gmobile communication standard, and so on). A wired interface may providenetwork interface functions for the playback device 102 to communicateover a wired connection with other devices in accordance with acommunication protocol (e.g., IEEE 802.3). While the network interface224 shown in FIG. 2A includes both wired and wireless interfaces, theplayback device 102 may in some implementations include only wirelessinterface(s) or only wired interface(s).

In general, the network interface 224 facilitates data flow between theplayback device 102 and one or more other devices on a data network. Forinstance, the playback device 102 may be configured to receive audiocontent over the data network from one or more other playback devices,network devices within a LAN, and/or audio content sources over a WAN,such as the Internet. In one example, the audio content and othersignals transmitted and received by the playback device 102 may betransmitted in the form of digital packet data comprising an InternetProtocol (IP)-based source address and IP-based destination addresses.In such a case, the network interface 224 may be configured to parse thedigital packet data such that the data destined for the playback device102 is properly received and processed by the playback device 102.

As shown in FIG. 2A, the playback device 102 also includes voiceprocessing components 220 that are operably coupled to one or moremicrophones 222. The microphones 222 are configured to detect sound(i.e., acoustic waves) in the environment of the playback device 102,which is then provided to the voice processing components 220. Morespecifically, each microphone 222 is configured to detect sound andconvert the sound into a digital or analog signal representative of thedetected sound, which can then cause the voice processing component 220to perform various functions based on the detected sound, as describedin greater detail below. In one implementation, the microphones 222 arearranged as an array of microphones (e.g., an array of six microphones).In some implementations, the playback device 102 includes more than sixmicrophones (e.g., eight microphones or twelve microphones) or fewerthan six microphones (e.g., four microphones, two microphones, or asingle microphones).

In operation, the voice-processing components 220 are generallyconfigured to detect and process sound received via the microphones 222,identify potential voice input in the detected sound, and extractdetected-sound data to enable a VAS, such as the VAS 190 (FIG. 1B), toprocess voice input identified in the detected-sound data. The voiceprocessing components 220 may include one or more analog-to-digitalconverters, an acoustic echo canceller (“AEC”), a spatial processor(e.g., one or more multi-channel Wiener filters, one or more otherfilters, and/or one or more beam former components), one or more buffers(e.g., one or more circular buffers), one or more wake-word engines, oneor more voice extractors, and/or one or more speech processingcomponents (e.g., components configured to recognize a voice of aparticular user or a particular set of users associated with ahousehold), among other example voice processing components. In exampleimplementations, the voice processing components 220 may include orotherwise take the form of one or more DSPs or one or more modules of aDSP. In this respect, certain voice processing components 220 may beconfigured with particular parameters (e.g., gain and/or spectralparameters) that may be modified or otherwise tuned to achieveparticular functions. In some implementations, one or more of the voiceprocessing components 220 may be a subcomponent of the processor 212.

In some implementations, the voice-processing components 220 may detectand store a user's voice profile, which may be associated with a useraccount of the MPS 100. For example, voice profiles may be stored asand/or compared to variables stored in a set of command information ordata table. The voice profile may include aspects of the tone orfrequency of a user's voice and/or other unique aspects of the user'svoice, such as those described in previously-referenced U.S. ApplicationPublication No. US-2017-0242653.

As further shown in FIG. 2A, the playback device 102 also includes powercomponents 227. The power components 227 can include at least anexternal power source interface 228, which may be coupled to a powersource (not shown) via a power cable or the like that physicallyconnects the playback device 102 to an electrical outlet or some otherexternal power source. Other power components may include, for example,transformers, converters, and like components configured to formatelectrical power.

In some implementations, the power components 227 of the playback device102 may additionally include an internal power source 229 (e.g., one ormore batteries) configured to power the playback device 102 without aphysical connection to an external power source. When equipped with theinternal power source 229, the playback device 102 may operateindependent of an external power source. In some such implementations,the external power source interface 228 may be configured to facilitatecharging the internal power source 229. As discussed before, a playbackdevice comprising an internal power source may be referred to herein asa “portable playback device.” On the other hand, a playback device thatoperates using an external power source may be referred to herein as a“stationary playback device,” although such a device may in fact bemoved around a home or other environment.

The playback device 102 can further include a user interface 240 thatmay facilitate user interactions independent of or in conjunction withuser interactions facilitated by one or more of the controller devices104. In various embodiments, the user interface 240 includes one or morephysical buttons and/or supports graphical interfaces provided on touchsensitive screen(s) and/or surface(s), among other possibilities, for auser to directly provide input. The user interface 240 may furtherinclude one or more of lights (e.g., LEDs) and the speakers to providevisual and/or audio feedback to a user.

As an illustrative example, FIG. 2B shows an example housing 230 of theplayback device 102 that includes a user interface in the form of acontrol area 232 at a top portion 234 of the housing 230. The controlarea 232 includes buttons 236 a-c for controlling audio playback, volumelevel, and other functions. The control area 232 also includes a button236 d for toggling the microphones 222 to either an on state or an offstate.

As further shown in FIG. 2B, the control area 232 is at least partiallysurrounded by apertures formed in the top portion 234 of the housing 230through which the microphones 222 (not visible in FIG. 2B) receive thesound in the environment of the playback device 102. The microphones 222may be arranged in various positions along and/or within the top portion234 or other areas of the housing 230 so as to detect sound from one ormore directions relative to the playback device 102.

While specific implementations of playback and network microphonedevices have been described above with respect to FIGS. 2A and 2B, thereare numerous configurations of devices, including, but not limited to,those having no UI, microphones in different locations, multiplemicrophone arrays positioned in different arrangements, and/or any otherconfiguration as appropriate to the requirements of a given application.For example, UIs and/or microphone arrays can be implemented in otherplayback devices and/or computing devices rather than those describedherein. Further, although a specific example of playback device 102 isdescribed with reference to MPS 100, one skilled in the art willrecognize that playback devices as described herein can be used in avariety of different environments, including (but not limited to)environments with more and/or fewer elements, without departing from thescope of the present disclosure. Likewise, MPS's as described herein canbe used with various different playback devices.

By way of illustration, SONOS, Inc. presently offers (or has offered)for sale certain playback devices that may implement certain of theembodiments disclosed herein, including a “PLAY:1,” “PLAY:3,” “PLAY:5,”“PLAYBAR,” “CONNECT:AMP,” “PLAYBASE,” “BEAM,” “CONNECT,” and “SUB.” Anyother past, present, and/or future playback devices may additionally oralternatively be used to implement the playback devices of exampleembodiments disclosed herein. Additionally, it should be understood thata playback device is not limited to the examples illustrated in FIG. 2Aor 2B or to the SONOS product offerings. For example, a playback devicemay include, or otherwise take the form of, a wired or wirelessheadphone set, which may operate as a part of the media playback system100 via a network interface or the like. In another example, a playbackdevice may include or interact with a docking station for personalmobile media playback devices. In yet another example, a playback devicemay be integral to another device or component such as a television, alighting fixture, or some other device for indoor or outdoor use.

b. Example Playback Device Configurations

FIGS. 3A-3E show example configurations of playback devices. Referringfirst to FIG. 3A, in some example instances, a single playback devicemay belong to a zone. For example, the playback device 102 c (FIG. 1A)on the Patio may belong to Zone A. In some implementations describedbelow, multiple playback devices may be “bonded” to form a “bondedpair,” which together form a single zone. For example, the playbackdevice 102 f (FIG. 1A) named “Bed 1” in FIG. 3A may be bonded to theplayback device 102 g (FIG. 1A) named “Bed 2” in FIG. 3A to form Zone B.Bonded playback devices may have different playback responsibilities(e.g., channel responsibilities). In another implementation describedbelow, multiple playback devices may be merged to form a single zone.For example, the playback device 102 d named “Bookcase” may be mergedwith the playback device 102 m named “Living Room” to form a single ZoneC. The merged playback devices 102 d and 102 m may not be specificallyassigned different playback responsibilities. That is, the mergedplayback devices 102 d and 102 m may, aside from playing audio contentin synchrony, each play audio content as they would if they were notmerged.

For purposes of control, each zone in the MPS 100 may be represented asa single user interface (“UI”) entity. For example, as displayed by thecontroller devices 104, Zone A may be provided as a single entity named“Portable,” Zone B may be provided as a single entity named “Stereo,”and Zone C may be provided as a single entity named “Living Room.”

In various embodiments, a zone may take on the name of one of theplayback devices belonging to the zone. For example, Zone C may take onthe name of the Living Room device 102 m (as shown). In another example,Zone C may instead take on the name of the Bookcase device 102 d. In afurther example, Zone C may take on a name that is some combination ofthe Bookcase device 102 d and Living Room device 102 m. The name that ischosen may be selected by a user via inputs at a controller device 104.In some embodiments, a zone may be given a name that is different thanthe device(s) belonging to the zone. For example, Zone B in FIG. 3A isnamed “Stereo” but none of the devices in Zone B have this name. In oneaspect, Zone B is a single UI entity representing a single device named“Stereo,” composed of constituent devices “Bed 1” and “Bed 2.” In oneimplementation, the Bed 1 device may be playback device 102 f in themaster bedroom 101 h (FIG. 1A) and the Bed 2 device may be the playbackdevice 102 g also in the master bedroom 101 h (FIG. 1A).

As noted above, playback devices that are bonded may have differentplayback responsibilities, such as playback responsibilities for certainaudio channels. For example, as shown in FIG. 3B, the Bed 1 and Bed 2devices 102 f and 102 g may be bonded so as to produce or enhance astereo effect of audio content. In this example, the Bed 1 playbackdevice 102 f may be configured to play a left channel audio component,while the Bed 2 playback device 102 g may be configured to play a rightchannel audio component. In some implementations, such stereo bondingmay be referred to as “pairing.”

Additionally, playback devices that are configured to be bonded may haveadditional and/or different respective speaker drivers. As shown in FIG.3C, the playback device 102 b named “Front” may be bonded with theplayback device 102 k named “SUB.” The Front device 102 b may render arange of mid to high frequencies, and the SUB device 102 k may renderlow frequencies as, for example, a subwoofer. When unbonded, the Frontdevice 102 b may be configured to render a full range of frequencies. Asanother example, FIG. 3D shows the Front and SUB devices 102 b and 102 kfurther bonded with Right and Left playback devices 102 a and 102 j,respectively. In some implementations, the Right and Left devices 102 aand 102 j may form surround or “satellite” channels of a home theatersystem. The bonded playback devices 102 a, 102 b, 102 j, and 102 k mayform a single Zone D (FIG. 3A).

In some implementations, playback devices may also be “merged.” Incontrast to certain bonded playback devices, playback devices that aremerged may not have assigned playback responsibilities, but may eachrender the full range of audio content that each respective playbackdevice is capable of. Nevertheless, merged devices may be represented asa single UI entity (i.e., a zone, as discussed above). For instance,FIG. 3E shows the playback devices 102 d and 102 m in the Living Roommerged, which would result in these devices being represented by thesingle UI entity of Zone C. In one embodiment, the playback devices 102d and 102 m may playback audio in synchrony, during which each outputsthe full range of audio content that each respective playback device 102d and 102 m is capable of rendering.

In some embodiments, a stand-alone NMD may be in a zone by itself. Forexample, the NMD 103 h from FIG. 1A is named “Closet” and forms Zone Iin FIG. 3A. An NMD may also be bonded or merged with another device soas to form a zone. For example, the NMD device 103 f named “Island” maybe bonded with the playback device 102 i Kitchen, which together formZone F, which is also named “Kitchen.” Additional details regardingassigning NMDs and playback devices as designated or default devices maybe found, for example, in previously referenced U.S. ApplicationPublication No. US-2017-0242653. In some embodiments, a stand-alone NMDmay not be assigned to a zone.

Zones of individual, bonded, and/or merged devices may be arranged toform a set of playback devices that playback audio in synchrony. Such aset of playback devices may be referred to as a “group,” “zone group,”“synchrony group,” or “playback group.” In response to inputs providedvia a controller device 104, playback devices may be dynamically groupedand ungrouped to form new or different groups that synchronously playback audio content. For example, referring to FIG. 3A, Zone A may begrouped with Zone B to form a zone group that includes the playbackdevices of the two zones. As another example, Zone A may be grouped withone or more other Zones C-I. The Zones A-I may be grouped and ungroupedin numerous ways. For example, three, four, five, or more (e.g., all) ofthe Zones A-I may be grouped. When grouped, the zones of individualand/or bonded playback devices may play back audio in synchrony with oneanother, as described in previously referenced U.S. Pat. No. 8,234,395.Grouped and bonded devices are example types of associations betweenportable and stationary playback devices that may be caused in responseto a trigger event, as discussed above and described in greater detailbelow.

In various implementations, the zones in an environment may be assigneda particular name, which may be the default name of a zone within a zonegroup or a combination of the names of the zones within a zone group,such as “Dining Room+Kitchen,” as shown in FIG. 3A. In some embodiments,a zone group may be given a unique name selected by a user, such as“Nick's Room,” as also shown in FIG. 3A. The name “Nick's Room” may be aname chosen by a user over a prior name for the zone group, such as theroom name “Master Bedroom.”

Referring back to FIG. 2A, certain data may be stored in the memory 213as one or more state variables that are periodically updated and used todescribe the state of a playback zone, the playback device(s), and/or azone group associated therewith. The memory 213 may also include thedata associated with the state of the other devices of the mediaplayback system 100, which may be shared from time to time among thedevices so that one or more of the devices have the most recent dataassociated with the system.

In some embodiments, the memory 213 of the playback device 102 may storeinstances of various variable types associated with the states.Variables instances may be stored with identifiers (e.g., tags)corresponding to type. For example, certain identifiers may be a firsttype “al” to identify playback device(s) of a zone, a second type “b1”to identify playback device(s) that may be bonded in the zone, and athird type “c1” to identify a zone group to which the zone may belong.As a related example, in FIG. 1A, identifiers associated with the Patiomay indicate that the Patio is the only playback device of a particularzone and not in a zone group. Identifiers associated with the LivingRoom may indicate that the Living Room is not grouped with other zonesbut includes bonded playback devices 102 a, 102 b, 102 j, and 102 k.Identifiers associated with the Dining Room may indicate that the DiningRoom is part of Dining Room+Kitchen group and that devices 103 f and 102i are bonded. Identifiers associated with the Kitchen may indicate thesame or similar information by virtue of the Kitchen being part of theDining Room+Kitchen zone group. Other example zone variables andidentifiers are described below.

In yet another example, the MPS 100 may include variables or identifiersrepresenting other associations of zones and zone groups, such asidentifiers associated with Areas, as shown in FIG. 3A. An Area mayinvolve a cluster of zone groups and/or zones not within a zone group.For instance, FIG. 3A shows a first area named “First Area” and a secondarea named “Second Area.” The First Area includes zones and zone groupsof the Patio, Den, Dining Room, Kitchen, and Bathroom. The Second Areaincludes zones and zone groups of the Bathroom, Nick's Room, Bedroom,and Living Room. In one aspect, an Area may be used to invoke a clusterof zone groups and/or zones that share one or more zones and/or zonegroups of another cluster. In this respect, such an Area differs from azone group, which does not share a zone with another zone group. Furtherexamples of techniques for implementing Areas may be found, for example,in U.S. application Ser. No. 15/682,506 filed Aug. 21, 2017 and titled“Room Association Based on Name,” and U.S. Pat. No. 8,483,853 filed Sep.11, 2007, and titled “Controlling and manipulating groupings in amulti-zone media system.” Each of these applications is incorporatedherein by reference in its entirety. In some embodiments, the MPS 100may not implement Areas, in which case the system may not storevariables associated with Areas.

The memory 213 may be further configured to store other data. Such datamay pertain to audio sources accessible by the playback device 102 or aplayback queue that the playback device (or some other playbackdevice(s)) may be associated with. In embodiments described below, thememory 213 is configured to store a set of command data for selecting aparticular VAS when processing voice inputs.

During operation, one or more playback zones in the environment of FIG.1A may each be playing different audio content. For instance, the usermay be grilling in the Patio zone and listening to hip hop music beingplayed by the playback device 102 c, while another user may be preparingfood in the Kitchen zone and listening to classical music being playedby the playback device 102 i. In another example, a playback zone mayplay the same audio content in synchrony with another playback zone. Forinstance, the user may be in the Office zone where the playback device102 n is playing the same hip-hop music that is being playing byplayback device 102 c in the Patio zone. In such a case, playbackdevices 102 c and 102 n may be playing the hip-hop in synchrony suchthat the user may seamlessly (or at least substantially seamlessly)enjoy the audio content that is being played out-loud while movingbetween different playback zones. Synchronization among playback zonesmay be achieved in a manner similar to that of synchronization amongplayback devices, as described in previously referenced U.S. Pat. No.8,234,395.

As suggested above, the zone configurations of the MPS 100 may bedynamically modified. As such, the MPS 100 may support numerousconfigurations. For example, if a user physically moves one or moreplayback devices to or from a zone, the MPS 100 may be reconfigured toaccommodate the change(s). For instance, if the user physically movesthe playback device 102 c from the Patio zone to the Office zone, theOffice zone may now include both the playback devices 102 c and 102 n.In some cases, the user may pair or group the moved playback device 102c with the Office zone and/or rename the players in the Office zoneusing, for example, one of the controller devices 104 and/or voiceinput. As another example, if one or more playback devices 102 are movedto a particular space in the home environment that is not already aplayback zone, the moved playback device(s) may be renamed or associatedwith a playback zone for the particular space.

Further, different playback zones of the MPS 100 may be dynamicallycombined into zone groups or split up into individual playback zones.For example, the Dining Room zone and the Kitchen zone may be combinedinto a zone group for a dinner party such that playback devices 102 iand 102 l may render audio content in synchrony. As another example,bonded playback devices in the Den zone may be split into (i) atelevision zone and (ii) a separate listening zone. The television zonemay include the Front playback device 102 b. The listening zone mayinclude the Right, Left, and SUB playback devices 102 a, 102 j, and 102k, which may be grouped, paired, or merged, as described above.Splitting the Den zone in such a manner may allow one user to listen tomusic in the listening zone in one area of the living room space, andanother user to watch the television in another area of the living roomspace. In a related example, a user may utilize either of the NMD 103 aor 103 b (FIG. 1B) to control the Den zone before it is separated intothe television zone and the listening zone. Once separated, thelistening zone may be controlled, for example, by a user in the vicinityof the NMD 103 a, and the television zone may be controlled, forexample, by a user in the vicinity of the NMD 103 b. As described above,however, any of the NMDs 103 may be configured to control the variousplayback and other devices of the MPS 100.

c. Example Controller Devices

FIG. 4A is a functional block diagram illustrating certain aspects of aselected one of the controller devices 104 of the MPS 100 of FIG. 1A.Controller devices in accordance with several embodiments can be used invarious systems, such as (but not limited to) an MPS as described inFIG. 1A. Such controller devices may also be referred to herein as a“control device” or “controller.” The controller device shown in FIG. 4Amay include components that are generally similar to certain componentsof the network devices described above, such as a processor 412, memory413 storing program software 414, at least one network interface 424,and one or more microphones 422. In one example, a controller device maybe a dedicated controller for the MPS 100. In another example, acontroller device may be a network device on which media playback systemcontroller application software may be installed, such as for example,an iPhone™, iPad™ or any other smart phone, tablet, or network device(e.g., a networked computer such as a PC or Mac™).

The memory 413 of the controller device 104 may be configured to storecontroller application software and other data associated with the MPS100 and/or a user of the system 100. The memory 413 may be loaded withinstructions in software 414 that are executable by the processor 412 toachieve certain functions, such as facilitating user access, control,and/or configuration of the MPS 100. The controller device 104 can beconfigured to communicate with other network devices via the networkinterface 424, which may take the form of a wireless interface, asdescribed above.

In one example, system information (e.g., such as a state variable) maybe communicated between the controller device 104 and other devices viathe network interface 424. For instance, the controller device 104 mayreceive playback zone and zone group configurations in the MPS 100 froma playback device, an NMD, or another network device. Likewise, thecontroller device 104 may transmit such system information to a playbackdevice or another network device via the network interface 424. In somecases, the other network device may be another controller device.

The controller device 104 may also communicate playback device controlcommands, such as volume control and audio playback control, to aplayback device via the network interface 424. As suggested above,changes to configurations of the MPS 100 may also be performed by a userusing the controller device 104. The configuration changes may includeadding/removing one or more playback devices to/from a zone,adding/removing one or more zones to/from a zone group, forming a bondedor merged player, separating one or more playback devices from a bondedor merged player, among others.

As shown in FIG. 4A, the controller device 104 can also include a userinterface 440 that is generally configured to facilitate user access andcontrol of the MPS 100. The user interface 440 may include atouch-screen display or other physical interface configured to providevarious graphical controller interfaces, such as the controllerinterfaces 440 a and 440 b shown in FIGS. 4B and 4C. Referring to FIGS.4B and 4C together, the controller interfaces 440 a and 440 b include aplayback control region 442, a playback zone region 443, a playbackstatus region 444, a playback queue region 446, and a sources region448. The user interface as shown is just one example of an interfacethat may be provided on a network device, such as the controller deviceshown in FIG. 4A, and accessed by users to control a media playbacksystem, such as the MPS 100. Other user interfaces of varying formats,styles, and interactive sequences may alternatively be implemented onone or more network devices to provide comparable control access to amedia playback system.

The playback control region 442 (FIG. 4B) may include selectable icons(e.g., by way of touch or by using a cursor) that, when selected, causeplayback devices in a selected playback zone or zone group to play orpause, fast forward, rewind, skip to next, skip to previous, enter/exitshuffle mode, enter/exit repeat mode, enter/exit cross fade mode, etc.The playback control region 442 may also include selectable icons that,when selected, modify equalization settings and/or playback volume,among other possibilities.

The playback zone region 443 (FIG. 4C) may include representations ofplayback zones within the MPS 100. The playback zones regions 443 mayalso include a representation of zone groups, such as the DiningRoom+Kitchen zone group, as shown. In some embodiments, the graphicalrepresentations of playback zones may be selectable to bring upadditional selectable icons to manage or configure the playback zones inthe MPS 100, such as a creation of bonded zones, creation of zonegroups, separation of zone groups, and renaming of zone groups, amongother possibilities.

For example, as shown, a “group” icon may be provided within each of thegraphical representations of playback zones. The “group” icon providedwithin a graphical representation of a particular zone may be selectableto bring up options to select one or more other zones in the MPS 100 tobe grouped with the particular zone. Once grouped, playback devices inthe zones that have been grouped with the particular zone will beconfigured to play audio content in synchrony with the playbackdevice(s) in the particular zone. Analogously, a “group” icon may beprovided within a graphical representation of a zone group. In thiscase, the “group” icon may be selectable to bring up options to deselectone or more zones in the zone group to be removed from the zone group.Other interactions and implementations for grouping and ungrouping zonesvia a user interface are also possible. The representations of playbackzones in the playback zone region 443 (FIG. 4C) may be dynamicallyupdated as playback zone or zone group configurations are modified.

The playback status region 444 (FIG. 4B) may include graphicalrepresentations of audio content that is presently being played,previously played, or scheduled to play next in the selected playbackzone or zone group. The selected playback zone or zone group may bevisually distinguished on a controller interface, such as within theplayback zone region 443 and/or the playback status region 444. Thegraphical representations may include track title, artist name, albumname, album year, track length, and/or other relevant information thatmay be useful for the user to know when controlling the MPS 100 via acontroller interface.

The playback queue region 446 may include graphical representations ofaudio content in a playback queue associated with the selected playbackzone or zone group. In some embodiments, each playback zone or zonegroup may be associated with a playback queue comprising informationcorresponding to zero or more audio items for playback by the playbackzone or zone group. For instance, each audio item in the playback queuemay comprise a uniform resource identifier (URI), a uniform resourcelocator (URL), or some other identifier that may be used by a playbackdevice in the playback zone or zone group to find and/or retrieve theaudio item from a local audio content source or a networked audiocontent source, which may then be played back by the playback device.

In one example, a playlist may be added to a playback queue, in whichcase information corresponding to each audio item in the playlist may beadded to the playback queue. In another example, audio items in aplayback queue may be saved as a playlist. In a further example, aplayback queue may be empty, or populated but “not in use” when theplayback zone or zone group is playing continuously streamed audiocontent, such as Internet radio that may continue to play untilotherwise stopped, rather than discrete audio items that have playbackdurations. In an alternative embodiment, a playback queue can includeInternet radio and/or other streaming audio content items and be “inuse” when the playback zone or zone group is playing those items. Otherexamples are also possible.

When playback zones or zone groups are “grouped” or “ungrouped,”playback queues associated with the affected playback zones or zonegroups may be cleared or re-associated. For example, if a first playbackzone including a first playback queue is grouped with a second playbackzone including a second playback queue, the established zone group mayhave an associated playback queue that is initially empty, that containsaudio items from the first playback queue (such as if the secondplayback zone was added to the first playback zone), that contains audioitems from the second playback queue (such as if the first playback zonewas added to the second playback zone), or a combination of audio itemsfrom both the first and second playback queues. Subsequently, if theestablished zone group is ungrouped, the resulting first playback zonemay be re-associated with the previous first playback queue or may beassociated with a new playback queue that is empty or contains audioitems from the playback queue associated with the established zone groupbefore the established zone group was ungrouped. Similarly, theresulting second playback zone may be re-associated with the previoussecond playback queue or may be associated with a new playback queuethat is empty or contains audio items from the playback queue associatedwith the established zone group before the established zone group wasungrouped. Other examples are also possible.

With reference still to FIGS. 4B and 4C, the graphical representationsof audio content in the playback queue region 446 (FIG. 4B) may includetrack titles, artist names, track lengths, and/or other relevantinformation associated with the audio content in the playback queue. Inone example, graphical representations of audio content may beselectable to bring up additional selectable icons to manage and/ormanipulate the playback queue and/or audio content represented in theplayback queue. For instance, a represented audio content may be removedfrom the playback queue, moved to a different position within theplayback queue, or selected to be played immediately, or after anycurrently playing audio content, among other possibilities. A playbackqueue associated with a playback zone or zone group may be stored in amemory on one or more playback devices in the playback zone or zonegroup, on a playback device that is not in the playback zone or zonegroup, and/or some other designated device. Playback of such a playbackqueue may involve one or more playback devices playing back media itemsof the queue, perhaps in sequential or random order.

The sources region 448 may include graphical representations ofselectable audio content sources and/or selectable voice assistantsassociated with a corresponding VAS. The VASes may be selectivelyassigned. In some examples, multiple VASes, such as AMAZON's Alexa,MICROSOFT's Cortana, etc., may be invokable by the same NMD. In someembodiments, a user may assign a VAS exclusively to one or more NMDs.For example, a user may assign a first VAS to one or both of the NMDs102 a and 102 b in the Living Room shown in FIG. 1A, and a second VAS tothe NMD 103 f in the Kitchen. Other examples are possible.

d. Example Audio Content Sources

The audio sources in the sources region 448 may be audio content sourcesfrom which audio content may be retrieved and played by the selectedplayback zone or zone group. One or more playback devices in a zone orzone group may be configured to retrieve for playback audio content(e.g., according to a corresponding URI or URL for the audio content)from a variety of available audio content sources. In one example, audiocontent may be retrieved by a playback device directly from acorresponding audio content source (e.g., via a line-in connection). Inanother example, audio content may be provided to a playback device overa network via one or more other playback devices or network devices. Asdescribed in greater detail below, in some embodiments audio content maybe provided by one or more media content services.

Example audio content sources may include a memory of one or moreplayback devices in a media playback system such as the MPS 100 of FIG.1, local music libraries on one or more network devices (e.g., acontroller device, a network-enabled personal computer, or anetworked-attached storage (“NAS”)), streaming audio services providingaudio content via the Internet (e.g., cloud-based music services), oraudio sources connected to the media playback system via a line-in inputconnection on a playback device or network device, among otherpossibilities.

In some embodiments, audio content sources may be added or removed froma media playback system such as the MPS 100 of FIG. 1A. In one example,an indexing of audio items may be performed whenever one or more audiocontent sources are added, removed, or updated. Indexing of audio itemsmay involve scanning for identifiable audio items in allfolders/directories shared over a network accessible by playback devicesin the media playback system and generating or updating an audio contentdatabase comprising metadata (e.g., title, artist, album, track length,among others) and other associated information, such as a URI or URL foreach identifiable audio item found. Other examples for managing andmaintaining audio content sources may also be possible.

e. Example Network Microphone Devices

FIG. 5 is a functional block diagram showing an NMD 503 configured inaccordance with embodiments of the disclosure. The NMD 503 includesvoice capture components (“VCC”, or collectively “voice processor 560”),a wake-word engine 570, and at least one voice extractor 572, each ofwhich can be operably coupled to the voice processor 560. The NMD 503further includes the microphones 222 and the at least one networkinterface 224 described above and may also include other components,such as audio amplifiers, interface, etc., which are not shown in FIG. 5for purposes of clarity.

The microphones 222 of the NMD 503 can be configured to provide detectedsound, SD, from the environment of the NMD 503 to the voice processor560. The detected sound SD may take the form of one or more analog ordigital signals. In example implementations, the detected sound SD maybe composed of a plurality of signals associated with respectivechannels 562 that are fed to the voice processor 560.

Each channel 562 may correspond to a particular microphone 222. Forexample, an NMD having six microphones may have six correspondingchannels. Each channel of the detected sound SD may bear certainsimilarities to the other channels but may differ in certain regards,which may be due to the position of the given channel's correspondingmicrophone relative to the microphones of other channels. For example,one or more of the channels of the detected sound SD may have a greatersignal to noise ratio (“SNR”) of speech to background noise than otherchannels.

As further shown in FIG. 5, the voice processor 560 includes an AEC 564,a spatial processor 566, and one or more buffers 568. In operation, theAEC 564 receives the detected sound SD and filters or otherwiseprocesses the sound to suppress echoes and/or to otherwise improve thequality of the detected sound SD. That processed sound may then bepassed to the spatial processor 566.

The spatial processor 566 is typically configured to analyze thedetected sound SD and identify certain characteristics, such as asound's amplitude (e.g., decibel level), frequency spectrum,directionality, etc. In one respect, the spatial processor 566 may helpfilter or suppress ambient noise in the detected sound SD from potentialuser speech based on similarities and differences in the constituentchannels 562 of the detected sound SD, as discussed above. As onepossibility, the spatial processor 566 may monitor metrics thatdistinguish speech from other sounds. Such metrics can include, forexample, energy within the speech band relative to background noise andentropy within the speech band—a measure of spectral structure—which istypically lower in speech than in most common background noise. In someimplementations, the spatial processor 566 may be configured todetermine a speech presence probability, examples of such functionalityare disclosed in U.S. patent application Ser. No. 15/984,073, filed May18, 2018, titled “Linear Filtering for Noise-Suppressed SpeechDetection,” and U.S. patent application Ser. No. 16/147,710, filed Sep.29, 2018, and titled “Linear Filtering for Noise-Suppressed SpeechDetection via Multiple Network Microphone Devices,” each of which isincorporated herein by reference in its entirety.

The wake-word engine 570 can be configured to monitor and analyzereceived audio to determine if any wake words are present in the audio.The wake-word engine 570 may analyze the received audio using a wakeword detection algorithm. If the wake-word engine 570 detects a wakeword, a network microphone device may process voice input contained inthe received audio. Example wake word detection algorithms accept audioas input and provide an indication of whether a wake word is present inthe audio. Many first- and third-party wake word detection processes areknown and commercially available. For instance, operators of a voiceservice may make their processes available for use in third-partydevices. Alternatively, a process may be trained to detect certainwake-words.

In some embodiments, the wake-word engine 570 runs multiple wake worddetection processes on the received audio simultaneously (orsubstantially simultaneously). As noted above, different voice services(e.g. AMAZON's Alexa®, APPLE's Siri®, MICROSOFT's Cortana®, GOOGLE'SAssistant, etc.) each use a different wake word for invoking theirrespective voice service. To support multiple services, the wake-wordengine 570 may run the received audio through the wake word detectionprocess for each supported voice service in parallel. In suchembodiments, the network microphone device 103 may include VAS selectorcomponents 574 configured to pass voice input to the appropriate voiceassistant service. In other embodiments, the VAS selector components 574may be omitted. In some embodiments, individual NMDs 103 of the MPS 100may be configured to run different wake word detection processesassociated with particular VASes. For example, the NMDs of playbackdevices 102 a and 102 b of the Living Room may be associated withAMAZON's ALEXA®, and be configured to run a corresponding wake worddetection process (e.g., configured to detect the wake word “Alexa” orother associated wake word), while the NMD of playback device 102 f inthe Kitchen may be associated with GOOGLE's Assistant, and be configuredto run a corresponding wake word detection process (e.g., configured todetect the wake word “OK, Google” or other associated wake word).

In some embodiments, a network microphone device may include speechprocessing components configured to further facilitate voice processing,such as by performing voice recognition trained to recognize aparticular user or a particular set of users associated with ahousehold. Voice recognition software may implement processes that aretuned to specific voice profile(s).

In operation, the one or more buffers 568—one or more of which may bepart of or separate from the memory 213 (FIG. 2A)—capture datacorresponding to the detected sound SD. More specifically, the one ormore buffers 568 capture detected-sound data that was processed by theupstream AEC 564 and spatial processor 566.

In general, the detected-sound data form a digital representation (i.e.,sound-data stream), SDS, of the sound detected by the microphones 222.In practice, the sound-data stream SDs may take a variety of forms. Asone possibility, the sound-data stream SDs may be composed of frames,each of which may include one or more sound samples. The frames may bestreamed (i.e., read out) from the one or more buffers 568 for furtherprocessing by downstream components, such as the wake-word engine 570and the voice extractor 572 of the NMD 503.

In some implementations, at least one buffer 568 captures detected-sounddata utilizing a sliding window approach in which a given amount (i.e.,a given window) of the most recently captured detected-sound data isretained in the at least one buffer 568 while older detected-sound dataare overwritten when they fall outside of the window. For example, atleast one buffer 568 may temporarily retain 20 frames of a soundspecimen at given time, discard the oldest frame after an expirationtime, and then capture a new frame, which is added to the 19 priorframes of the sound specimen.

In practice, when the sound-data stream SDs is composed of frames, theframes may take a variety of forms having a variety of characteristics.As one possibility, the frames may take the form of audio frames thathave a certain resolution (e.g., 16 bits of resolution), which may bebased on a sampling rate (e.g., 44,100 Hz). Additionally, oralternatively, the frames may include information corresponding to agiven sound specimen that the frames define, such as metadata thatindicates frequency response, power input level, signal-to-noise ratio,microphone channel identification, and/or other information of the givensound specimen, among other examples. Thus, in some embodiments, a framemay include a portion of sound (e.g., one or more samples of a givensound specimen) and metadata regarding the portion of sound. In otherembodiments, a frame may only include a portion of sound (e.g., one ormore samples of a given sound specimen) or metadata regarding a portionof sound.

The voice processor 560 can also include at least one lookback buffer569, which may be part of or separate from the memory 213 (FIG. 2A). Inoperation, the lookback buffer 569 can store sound metadata that isprocessed based on the detected-sound data SD received from themicrophones 222. As noted above, the microphones 224 can include aplurality of microphones arranged in an array. The sound metadata caninclude, for example: (1) frequency response data for individualmicrophones of the array, (2) an echo return loss enhancement measure(i.e., a measure of the effectiveness of the acoustic echo canceller(AEC) for each microphone), (3) a voice direction measure; (4)arbitration statistics (e.g., signal and noise estimates for the spatialprocessing streams associated with different microphones); and/or (5)speech spectral data (i.e., frequency response evaluated on processedaudio output after acoustic echo cancellation and spatial processinghave been performed). Other sound metadata may also be used to identifyand/or classify noise in the detected-sound data SD. In at least someembodiments, the sound metadata may be transmitted separately from thesound-data stream SDS, as reflected in the arrow extending from thelookback buffer 569 to the network interface 224. For example, the soundmetadata may be transmitted from the lookback buffer 569 to one or moreremote computing devices separate from the VAS which receives thesound-data stream SDS. In some embodiments, for example, the metadatacan be transmitted to a remote service provider for analysis toconstruct or modify a noise classifier, as described in more detailbelow.

In any case, components of the NMD 503 downstream of the voice processor560 may process the sound-data stream SDS. For instance, the wake-wordengine 570 can be configured to apply one or more identificationprocesses to the sound-data stream SDs (e.g., streamed sound frames) tospot potential wake words in the detected-sound SD. When the wake-wordengine 570 spots a potential wake word, the wake-word engine 570 canprovide an indication of a “wake-word event” (also referred to as a“wake-word trigger”) to the voice extractor 572 in the form of signalSw.

In response to the wake-word event (e.g., in response to a signal Swfrom the wake-word engine 570 indicating the wake-word event), the voiceextractor 572 can be configured to receive and format (e.g., packetize)the sound-data stream SDS. For instance, the voice extractor 572packetizes the frames of the sound-data stream SDS into messages. Thevoice extractor 572 can transmit or stream these messages, Mv, that maycontain voice input in real time or near real time, to a remote VAS,such as the VAS 190 (FIG. 1B), via the network interface 218.

The VAS can be configured to process the sound-data stream SDS containedin the messages Mv sent from the NMD 503. More specifically, the VAS canbe configured to identify voice input based on the sound-data streamSDS. Referring to FIG. 6A, a voice input 680 may include a wake-wordportion 680 a and an utterance portion 680 b. The wake-word portion 680a can correspond to detected sound that caused the wake-word event. Forinstance, the wake-word portion 680 a can correspond to detected soundthat caused the wake-word engine 570 to provide an indication of awake-word event to the voice extractor 572. The utterance portion 680 bcan correspond to detected sound that potentially comprises a userrequest following the wake-word portion 680 a.

As an illustrative example, FIG. 6B shows an example first soundspecimen. In this example, the sound specimen corresponds to thesound-data stream SDs (e.g., one or more audio frames) associated withthe spotted wake word 680 a of FIG. 6A. As illustrated, the examplefirst sound specimen comprises sound detected in the playback device 102i's environment (i) immediately before a wake word was spoken, which maybe referred to as a pre-roll portion (between times to and t₁), (ii)while the wake word was spoken, which may be referred to as a wake-meterportion (between times t₁ and t₂), and/or (iii) after the wake word wasspoken, which may be referred to as a post-roll portion (between timest₂ and t₃). Other sound specimens are also possible.

Typically, the VAS may first process the wake-word portion 680 a withinthe sound-data stream SDs to verify the presence of the wake word. Insome instances, the VAS may determine that the wake-word portion 680 acomprises a false wake word (e.g., the word “Election” when the word“Alexa” is the target wake word). In such an occurrence, the VAS maysend a response to the NMD 503 (FIG. 5) with an indication for the NMD503 to cease extraction of sound data, which may cause the voiceextractor 572 to cease further streaming of the detected-sound data tothe VAS. The wake-word engine 570 may resume or continue monitoringsound specimens until another potential wake word, leading to anotherwake-word event. In some implementations, the VAS may not process orreceive the wake-word portion 680 a but instead processes only theutterance portion 680 b.

In any case, the VAS processes the utterance portion 680 b to identifythe presence of any words in the detected-sound data and to determine anunderlying intent from these words. The words may correspond to acertain command and certain keywords 684 (identified individually inFIG. 6A as a first keyword 684 a and a second keyword 684 b). A keywordmay be, for example, a word in the voice input 680 identifying aparticular device or group in the MPS 100. For instance, in theillustrated example, the keywords 684 may be one or more wordsidentifying one or more zones in which the music is to be played, suchas the Living Room and the Dining Room (FIG. 1A).

To determine the intent of the words, the VAS is typically incommunication with one or more databases associated with the VAS (notshown) and/or one or more databases (not shown) of the MPS 100. Suchdatabases may store various user data, analytics, catalogs, and otherinformation for natural language processing and/or other processing. Insome implementations, such databases may be updated for adaptivelearning and feedback for a neural network based on voice-inputprocessing. In some cases, the utterance portion 680 b may includeadditional information, such as detected pauses (e.g., periods ofnon-speech) between words spoken by a user, as shown in FIG. 6A. Thepauses may demarcate the locations of separate commands, keywords, orother information spoke by the user within the utterance portion 680 b.

Based on certain command criteria, the VAS may take actions as a resultof identifying one or more commands in the voice input, such as thecommand 682. Command criteria may be based on the inclusion of certainkeywords within the voice input, among other possibilities.Additionally, or alternatively, command criteria for commands mayinvolve identification of one or more control-state and/or zone-statevariables in conjunction with identification of one or more particularcommands. Control-state variables may include, for example, indicatorsidentifying a level of volume, a queue associated with one or moredevices, and playback state, such as whether devices are playing aqueue, paused, etc. Zone-state variables may include, for example,indicators identifying which, if any, zone players are grouped.

After processing the voice input, the VAS may send a response to the MPS100 with an instruction to perform one or more actions based on anintent it determined from the voice input. For example, based on thevoice input, the VAS may direct the MPS 100 to initiate playback on oneor more of the playback devices 102, control one or more of thesedevices (e.g., raise/lower volume, group/ungroup devices, etc.), turnon/off certain smart devices, among other actions. After receiving theresponse from the VAS, the wake-word engine 570 the NMD 503 may resumeor continue to monitor the sound-data stream SIDS until it spots anotherpotential wake-word, as discussed above.

Referring back to FIG. 5, in multi-VAS implementations, the NMD 503 mayinclude a VAS selector 574 (shown in dashed lines) that is generallyconfigured to direct the voice extractor's extraction and transmissionof the sound-data stream SDS to the appropriate VAS when a givenwake-word is identified by a particular wake-word engine, such as thefirst wake-word engine 570 a, the second wake-word engine 570 b, or theadditional wake-word engine 571. In such implementations, the NMD 503may include multiple, different wake-word engines and/or voiceextractors, each supported by a particular VAS. Similar to thediscussion above, each wake-word engine may be configured to receive asinput the sound-data stream SDs from the one or more buffers 568 andapply identification algorithms to cause a wake-word trigger for theappropriate VAS. Thus, as one example, the first wake-word engine 570 amay be configured to identify the wake word “Alexa” and cause the NMD503 to invoke the AMAZON VAS when “Alexa” is spotted. As anotherexample, the second wake-word engine 570 b may be configured to identifythe wake word “Ok, Google” and cause the NMD 503 to invoke the GOOGLEVAS when “Ok, Google” is spotted. In single-VAS implementations, the VASselector 574 may be omitted.

In additional or alternative implementations, the NMD 503 may includeother voice-input identification engines 571 (shown in dashed lines)that enable the NMD 503 to operate without the assistance of a remoteVAS. As an example, such an engine may identify in detected soundcertain commands (e.g., “play,” “pause,” “turn on,” etc.) and/or certainkeywords or phrases, such as the unique name assigned to a givenplayback device (e.g., “Bookcase,” “Patio,” “Office,” etc.). In responseto identifying one or more of these commands, keywords, and/or phrases,the NMD 503 may communicate a signal (not shown in FIG. 5) that causesthe audio processing components 216 (FIG. 2A) to perform one or moreactions. For instance, when a user says “Hey Sonos, stop the music inthe office,” the NMD 503 may communicate a signal to the office playbackdevice 102 n, either directly, or indirectly via one or more otherdevices of the MPS 100, which causes the office device 102 n to stopaudio playback. Reducing or eliminating the need for assistance from aremote VAS may reduce latency that might otherwise occur when processingvoice input remotely. In some cases, the identification algorithmsemployed may be configured to identify commands that are spoken withouta preceding wake word. For instance, in the example above, the NMD 503may employ an identification algorithm that triggers an event to stopthe music in the office without the user first saying “Hey Sonos” oranother wake word.

III. Localizing a Portable Device

Systems and methods in accordance with numerous embodiments can be usedto localize portable devices in networked device systems. Unlike otherlocation technologies, processes in accordance with some embodiments canmaintain control over both the roaming device (i.e., the portable devicethat is being moved around) and the reference devices (e.g., stationaryplayback devices, controllers, etc.). As a result, a greater level ofcontrol over the devices within the device ecosystem can be leveraged todetermine a relative location for a portable device with a high degreeof accuracy in challenging environments (e.g., indoor environments withall types of obstructions).

In certain embodiments, localizing a portable device can be performed atthe portable device that is being located and/or at reference devices(e.g., a coordinator device) in an MPS. Localization processes inaccordance with several embodiments can be distributed across multipledevices (e.g., portable devices, stationary devices, remote devices,etc.). In several embodiments, a coordinator device is designated tocollect and store signal information from reference devices and/or fromthe portable device. Coordinator devices in accordance with numerousembodiments can be selected from the available devices of an MPS basedon one or more of several factors, including (but not limited to) RSSIof signals received from the portable device at the reference devices,frequency of use, device specifications (e.g., number of processorcores, processor clock speed, processor cache size, non-volatile memorysize, volatile memory size, etc.). For example, a particular player canbe selected as a coordinator device based on how long the processor hasbeen idle, so as not to interfere with the operation of any otherdevices during playback (e.g., selecting a speaker sitting in a guestbedroom that is used infrequently). In certain embodiments, coordinatordevices can include the portable device that is being located.

In a number of embodiments, localizing a portable device (e.g., aportable playback device, a smartphone, a tablet, etc.) can be used toidentify a relative location for the portable device based on a numberof reference devices in an MPS. Reference devices in accordance with anumber of embodiments can include stationary devices and/or portabledevices. As the localizing of a portable device is not an absolutelocation, but rather a location relative to the locations of otherreference devices, processes in accordance with many embodiments can beused to determine a nearest device, even when one or more of thereference devices is also portable.

An example of a process for localizing a portable device in a networkedsensor system in accordance with an embodiment is conceptuallyillustrated in FIG. 7. Process 700 identifies (705) characteristics ofsignals transmitted between reference devices. Process 700 alsoidentifies (710) characteristics of signals transmitted between aportable device and the reference devices.

In certain embodiments, signals are transmitted when each referencedevice (e.g., network players, NMDs, etc.) and/or portable deviceperforms a wireless (e.g., WI-FI) scan. Wireless scans in accordancewith numerous embodiments can include broadcasting a first wirelesssignal that causes other wireless devices to respond with a secondsignal. In a number of embodiments, wireless radios in each device canprovide, as a result of a wireless scan, signal information, which caninclude (but is not limited to) an indication of which devicesresponded, an indication of how long ago the scan was performed/how longago a device responded, and/or received signal strength indicator (RSSI)values associated with the response from a particular device. Signalinformation in accordance with certain embodiments is gathered in pairsbetween all of the devices.

Processes in accordance with certain embodiments can scan periodically,allowing the devices to maintain a history of signals received from theother devices. Reference and/or portable devices in accordance withseveral embodiments can scan for known devices and collectcharacteristics (e.g., RSSI) in a buffer (e.g., a ring buffer) andcalculate statistics (e.g., weighted averages, variances, etc.) based ona history of collected signal characteristics. In some embodiments,signal characteristics and/or calculated statistics can be identified byeach of the devices, pre-processed, and transmitted to a coordinatordevice. Coordinator devices in accordance with various embodiments cancollect and store signal information from reference devices and/or theportable device. In many embodiments, identified signal characteristicsand/or calculated statistics of the signals can be stored in a matrix,that stores values for a given characteristic (e.g., RSSI).

An example of a matrix data structure for signal strength in a system isillustrated in FIG. 8. In this example, data structure 800 includesvarious data that can be used to store various system and signalinformation. Data structure 800 includes matrix 805 (“ra_mean”), withRSSI measurements from each of five reference devices. Matrix 805includes five rows and six columns, where the intersection of each rowand column represents a RSSI for a signal between a transmittingreference device (across the top of the matrix) and a receivingreference device (down the side of the matrix). The sixth columncontains values for signals from the portable device to each of the fivereference devices. The diagonals of the matrix are zeros (as a devicedoes not transmit a signal to itself). In many cases, matrices caninclude measurements between all devices of a networked system. Forlarge networks, systems in accordance with numerous embodiments can berepresented by a sparse matrix, where the entries are blank for stationswhere signal is not received and/or when received signals are below somethreshold value.

In numerous embodiments, the captured signal information is noisy data(e.g., raw RSSI values) that may need to be cleaned. Cleaning noisy datain accordance with various embodiments can include computing a weightedaverage of historic RSSI values for each signal path to reduce some ofthe high-frequency noise common in RSSI values. In a number ofembodiments, the weighting factor can be based on timestamps of eachRSSI value (e.g., weighting weight recent RSSI values more heavily andreducing the weight of older RSSI values). Timestamps in accordance withvarious embodiments of the invention can include timestamps for when asignal is detected at a receiver and/or transmitted from a sender.

Process 700 normalizes (715) the measured signal characteristics toestimate signal path characteristics. In many embodiments, normalizingthe data can help to account for differences in the constructions of theWI-FI radios and front-end circuitries of each device based on theassumption that RSSI values associated with a signal transmitted frompoint A to point B should be approximately equal to the RSSI valuesassociated with the same signal being transmitted in the oppositedirection from point B to point A. Normalizing signal characteristics inaccordance with some embodiments can include calculating an average ofthe sent and received signals of a signal path between two devices(e.g., a portable device and a reference device, and/or between tworeference devices). Processes in accordance with numerous embodimentscan compare RSSI values associated with each pair of signal paths (i.e.,paths to and from another reference device) to identify an offset inRSSI values. In certain embodiments, identified offsets in RSSI valuescan be used as a basis to normalize the values to account for thedifferences in the construction of the radios.

An example of normalizing signal strengths between two devices isillustrated in FIG. 9. The first chart 905 illustrates both the measuredRSSI values associated with a transmission from a first device (e.g., aSonos Beam) to a different, second device (e.g., a Sonos One) as well asthe RSSI values associated with a transmission from the second deviceback to the first device. In this example, the measured signals in thetwo directions between the devices are different, even though the signalpath length between the devices remains the same. The second chart 910illustrates a graph of the difference between the recorded signals.

Process 700 estimates (720) a likelihood that a portable device is in aparticular location based on the estimated signal path characteristics.For example, processes in accordance with various embodiments cancompute a probability that a given player and/or smartphone executingthe Sonos app is at/near a particular location (e.g., near a particularstationary Sonos player). This computation may be performed by a Sonosplayer in the system and/or the controller on the smartphone. Forexample, a single player may aggregate all of the information (e.g.,RSSI values) for the computation, perform the computation, and providethe result (e.g., to the Sonos controller app). In certain embodiments,a machine learning model is trained to estimate the likelihoods based onsignal information as input that is labeled with a nearest referencedevice.

From an intuitive standpoint, the stronger the RSSI values associatedwith a given signal path, the shorter the length of the signal path. Forexample, if the RSSI values associated with the signal path from aroaming device to a player P1 are high, the roaming device is likelynear player P1.

Because RSSI values can be obtained for a large number of signal paths,processes in accordance with a variety of embodiments can layer onadditional logic to confirm that the roaming device is actually near agiven device. In this example, if the roaming device is actually quiteclose to P1, the RSSI values associated with the signal path from theroaming device to a second player P2 should be substantially similar tothe values associated with the signal path from the first player P1 toP2. Similarly, if the roaming device is actually quite close to P1, theRSSI values for the path from the remote device to a third device P3should be substantially similar to the RSSI values for the path from P1to P3. Accordingly, processes in accordance with numerous embodimentscan analyze RSSI values associated with multiple different signal pathsin an MPS to come up with a probability that a roaming device is near agiven stationary device.

Processes in accordance with numerous embodiments can estimatelikelihoods based on a physical/probabilistic model. In severalembodiments, signal attenuation can be used as a basis, but can bemodified to feed a probabilistic model. Processes in accordance withvarious embodiments can use the entire system (or a subset of a system)as the distance metric for total attenuation (A) since the decayconstant (t) and Euclidean distance (D) are entangled within the system.

S _(AB) =T _(A) R _(B) A _(AB)

A _(AB) =e ^(−D) ^(AB) ^(/τ)

where T is transmission, R is reception fraction, A is totalattenuation, S is recorded signal, D is Euclidean distance, and T is adecay constant. The total attenuation (A) is assumed identical along asame path (i.e., in both directions along the path). The notationidentifies directionality of a transmission:

S _((Transmitter)(Receiver)): RSSI between devices

In the case of two reference devices, the probability P_(A) that theportable device is nearest to a device A, can be computed as:

$P_{A} \propto \frac{A_{AB}}{A_{BC}} \propto \frac{S_{CA}/S_{BA}}{S_{CB}/S_{AB}}$

The probability P_(B) that the portable device is nearest to device B,can be computed as:

P _(B)∝(1−P _(A))

When there are more than three devices, physical/probabilistic models inaccordance with various embodiments can provide a baseline that providesa more robust probability of the likely closest device. More generally,in numerous embodiments, the probability that a portable device isnearest to a given device F can be calculated as:

$P_{F} \propto {\frac{S_{MF}}{S_{MR}}\frac{S_{FR}}{S_{RF}}}$

where M is the moving device, F is a fixed station, and R is a referencestation. The probability for the reference station R can then becalculated as:

$P_{j} \propto {\sum\limits_{{i = 1},{i \neq j}}^{n}\frac{1 - P_{i}}{n - 1}}$

In numerous embodiments, the likelihood that a portable device isnearest to a given device changes as the device moves around. An exampleof changing likelihoods based on movement of a portable device isillustrated in FIG. 10. The first chart 1005 shows the calculatedprobabilities for each of three reference devices (SB3, SB4, and SB5)and how they change over a period of time. The second chart 1010 showsthe changes in the nearest device, based on the calculatedprobabilities, over the same period of time.

In certain embodiments, the estimated locations of portable devices canbe used in a variety of applications, such as (but not limited to)controlling a nearest reference device, presenting an ordered list ofnearest reference devices to a GUI of the portable device, monitoringmovement through a household, etc. Estimated locations in accordancewith numerous embodiments can be used as inputs to another predictionmodel that can be used to identify a target reference device based onthe location information. In various embodiments, a probability matrixfor reference devices in an MPS can be used as an input to a machinelearning model that can predict a target device (or a device a userwishes to interact with). The resulting matrix in accordance with manyembodiments is an n×n matrix of probabilities that can be used to definea robust metric for identifying room localization. Predicting targetdevices is discussed in greater detail below.

While specific processes for localizing a portable device are describedabove, any of a variety of processes can be utilized as appropriate tothe requirements of specific applications. In certain embodiments, stepsmay be executed or performed in any order or sequence not limited to theorder and sequence shown and described. In a number of embodiments, someof the above steps may be executed or performed substantiallysimultaneously where appropriate or in parallel to reduce latency andprocessing times. In some embodiments, one or more of the above stepsmay be omitted.

An example of localizing a portable device in a networked sensor systemin accordance with an embodiment is illustrated in four stages 1105-1120of FIG. 11. The stages of this example show a networked system with aportable device 1125, and reference devices 1140-1144. In the firststage, reference devices 1140-1144 transmit and receive signals amongeach other. Signals in accordance with some embodiments are part of awireless scan that is periodically performed by each reference device tomaintain information regarding the other reference devices in an MPS.

In the second stage 1110, portable device 1125 transmits signals toreference devices 1140-1144. In this example, the portable devicetransmits to the reference devices, but portable devices in accordancewith a number of embodiments can receive signals transmitted by thereference devices. Signal characteristics and/or statistics can begathered and transmitted to a coordinator device for further processing.

In the third stage 1115, reference device 1140 has been selected as acoordinator device. Coordinator devices in accordance with numerousembodiments are designated to collect and process signal informationfrom an MPS, and can be selected from the available devices of an MPSbased on one or more of several factors, including (but not limited to)RSSI of signals received from the portable device at the referencedevices, frequency of use, etc. The third stage 1115 shows thatreference devices 1142 and 1144 send their signal information tocoordinator device 1140. In many embodiments, coordinator devices can beplayback devices, portable devices, controller devices, etc. Forexample, in some embodiments, once the individual devices have collectedthe proper signals from each other, the individual devices can send thesignal information to the portable coordinator device, which can thenprocess the signal information to locate the portable device. Processingin accordance with several embodiments can include (but is not limitedto) cleaning the raw signal data, calculating statistics, normalizingRSSI values, and/or passing the signal information through a machinelearning model to determine a nearest reference device.

In the fourth stage 1120, coordinator device 1140 communicates back withreference device 1144, which was identified as the nearest device (orgroup of devices). While in this example coordinator device 1140communicates with only the target reference device, coordinator devicesin accordance with numerous embodiments can communicate with one or moreof the nearest devices, one or more portable devices, and/or anycombination thereof without departing from the scope of the presentdisclosure. In many embodiments, coordinator devices can communicatevarious information to the devices, such as (but not limited to)playback controls, recommended content, a sorted lists of targetdevices, etc.

As can readily be appreciated the specific system used to localize amobile device is largely dependent upon the requirements of a givenapplication and should not be considered as limited to any specificcomputing system(s) implementation.

a. Localization Element

An example of a localization element that can execute instructions toperform processes that locate portable devices in a networked devicesystem (e.g., an MPS) in accordance with various embodiments is shown inFIG. 12. Localization elements in accordance with many embodiments caninclude various networked devices, such as (but not limited to) one ormore of portable devices, stationary playback devices, wirelessspeakers, Internet of Things (IoT) devices, cloud services, servers,and/or personal computers. In this example, localization element 1200includes processor 1205, peripherals 1210, network interface 1215, andmemory 1220. One skilled in the art will recognize that a particularlocalization element may include other components that are omitted forbrevity without departing from the scope of the present disclosure.

The processor 1205 can include (but is not limited to) a processor,microprocessor, controller, or a combination of processors,microprocessor, and/or controllers that performs instructions stored inthe memory 1220 to manipulate data stored in the memory. Processorinstructions can configure the processor 1205 to perform processes inaccordance with certain embodiments.

Peripherals 1210 can include any of a variety of components forcapturing data, such as (but not limited to) cameras, displays, and/orsensors. In a variety of embodiments, peripherals can be used to gatherinputs and/or provide outputs. Network interface 1215 allowslocalization element 1200 to transmit and receive data over a networkbased upon the instructions performed by processor 1205. Peripheralsand/or network interfaces in accordance with many embodiments can beused to gather inputs (e.g., signals, user inputs, and/or contextinformation) that can be used to localize portable devices.

Memory 1220 includes a localization application 1225, signal data 1230,and model data 1235. Localization applications in accordance withseveral embodiments can be used to localize portable devices in anetworked system of devices. In numerous embodiments, signal data caninclude data captured at the localization element. Signal data inaccordance with a number of embodiments can include signal informationreceived from other reference devices and/or portable devices. Modeldata in accordance with some embodiments can include parameters for amachine learning model trained to generate probabilistic locationinformation based on input signal characteristics.

Although a specific example of a localization element 1200 isillustrated in FIG. 12, any of a variety of such elements can beutilized to perform processes similar to those described herein asappropriate to the requirements of specific applications in accordancewith embodiments.

b. Localization Application

FIG. 13 illustrates an example of a localization application inaccordance with an embodiment. Localization applications in accordancewith a variety of embodiments can be used for locating portable devicesin a networked system. In this example, the localization applicationincludes transmission engine 1305, receiver engine 1310, data collectionengine 1315, normalizing engine 1320, likelihood engine 1325, and outputengine 1330. As can readily be appreciated, localization applicationscan be implemented using any of a variety of configurations appropriateto the requirements of specific applications.

Transmission engines and receiver engines in accordance with a varietyof embodiments can be used to transmit and receive signals betweenreference devices and/or portable devices. In many embodiments,transmission engines can broadcast wireless signals as part of aperiodic wireless scan. Receiver engines in accordance with certainembodiments can receive signals broadcast by other reference devicesand/or the portable device.

Data collection engine 1315 collects signal data from the other devicesof the MPS. In many embodiments, data collection engines can alsoperform some pre-processing and/or cleaning of the signal data. In anumber of embodiments, pre-processing and/or cleaning are distributedbetween a coordinator device and one or more reference devices. Datacollection engines in accordance with a number of embodiments canmaintain a history of signal data in order to compute statistics (e.g.,weighted averages) of the historic signal data.

Once the data has been collected, normalizing engine 1320 can normalizethe collected data. Normalizing signal characteristics in accordancewith some embodiments can include calculating an average of the sent andreceived signals of a signal path between two devices (e.g., a portabledevice and a reference device, and/or between two reference devices).

Likelihood engines in accordance with a number of embodiments cancompute the likelihood that a portable device is nearest to a particularreference device of an MPS. In various embodiments, likelihood enginescan include a physical/probabilistic model to estimate likelihoods basedon signal strengths for signals received from reference devices and theportable device in relation to each other. Likelihood engines inaccordance with a variety of embodiments can calculate signal strengthratios for signals received at various devices in an MPS, where theprobability that the portable device is nearest to a particularreference device is a ratio between a first signal ratio for signalsreceived at the particular reference device and a second signal ratiofor signals received at a second reference device.

In a variety of embodiments, output engines can provide outputs to adisplay and/or transmit instructions and/or information to devices in anMPS based on the computed likelihoods. Outputs in accordance with someembodiments can include but are not limited notifications, alerts,playback controls, ordered device listings, etc. In various embodiments,outputs can include a probability matrix that can be used as an input toa prediction model. Prediction models in accordance with numerousembodiments are discussed in greater detail below.

Although a specific example of a localization application 1300 isillustrated in FIG. 13, any of a variety of localization applicationscan be utilized to perform processes for localizing portable devicessimilar to those described herein as appropriate to the requirements ofspecific applications in accordance with embodiments. In someembodiments, one or more of the above elements may be omitted and/oradditional elements may be added.

IV. User Interaction Prediction

Systems and methods in accordance with several embodiments can be usedto train prediction models and/or to predict target devices usingtrained prediction models. For example, a coordinator device inaccordance with various embodiments can predict a stationary playbackdevice that a user would want to interact with next (which may or maynot be the player closest to them) using a machine learning model thatlearns over time.

Processes in accordance with numerous embodiments can build and train amachine learning model that can receive signal characteristics (e.g.,raw or pre-processed RSSI values) as input and generate, based on thosesignal characteristics, an indication of the reference device (e.g., astationary player) that a person is standing near. In certainembodiments, the information that the person is standing near aparticular device can be used to drive new user experiences (e.g.,highlighting the speakers in the Sonos app a user is standing closestto, shifting music to play on different speakers as a user walks througha space). However, in some cases, a user may want to control a speakerother than the one to which they are closest. For example, the user mayalways turn on the kitchen Sonos speakers first thing in the morningfrom their bedroom because that is the room they intend to go to next.As a result, the underlying assumption that a user wants to alwaysinteract with the closest player doesn't hold in many situations.

In some embodiments, user interaction information can be leveraged toidentify these types of unique user behaviors. In many embodiments, newtraining data can be constructed from these types of unique userbehaviors that can be used to train (and/or retrain) a machine learningmodel to identify the desired interactions. In numerous embodiments, apredictive machine model is initially trained to identify the closeststationary speaker, and is then retrained with new training data that isspecific to the user and/or household. In certain embodiments, thepredictive machine model is initially trained using a singular valuedecomposition of the probability matrix as input and an online trainingprocess is used as more data is collected to build a more sophisticatedmodel. In many embodiments, various local online models can be used asinteractions are refined; such that data can remain private and does notneed to be sent out to remote computing devices. Thus, the predictivemodel can start to take into account the particular patterns of theuser. As a result, predictive machine learning models in accordance withvarious embodiments can adapt over time to each particular user so thatthe “best guess” of the device they want to interact with next may getbetter over time.

Predicting a target device (or user interaction) in accordance withvarious embodiments can be used to simplify controllers in an MPS. Forexample, upon detecting activation of a “Play/Pause” button in the SonosApp, the Play/Pause command can be automatically applied only to thoseplayers that are proximate the user's smartphone. In another example,the particular player (or zone group) that an individual is standingnear can be emphasized (e.g., highlighted, made bold, etc.) in the SonosApp to help the individual to control the correct zone group (instead ofanother zone group).

In a number of embodiments, predicted target devices can be used to swapcontent between devices. For example, processes in accordance with manyembodiments can allow a user wearing headphones to transition (e.g., viaa gesture on the capacitive touch sensor on the headphones) music frombeing played on the headphones to being played on nearby speakers.

An example of a process for training a prediction model in accordancewith an embodiment is conceptually illustrated in FIG. 14. In a numberof embodiments, prediction models operate and/or are re-trained locallyat one of the devices in an MPS, allowing the prediction models tocontinually adjust to a user's interactions.

Process 1400 monitors (1405) an MPS. In a variety of embodiments,monitoring an MPS can include monitoring (but is not limited to) thelocation (or trajectory) of one or more portable devices within an MPS,user interactions with devices (e.g., portable, stationary, playback,controllers, etc.) of the MPS, and/or predictions of user interactionsby a prediction model of the MPS.

Process 1400 determines (1410) whether to capture a training data samplebased on the monitoring of the MPS. The determination of whether tocapture training data can be based on a number of different criteria.For example, in certain embodiments, processes can determine to capturea training data sample when a confidence level for a prediction for thelocation of a portable device is below a particular threshold. Therewill likely be instances where the output of a prediction model isfluctuating between two states or otherwise has a low confidence answer.In these scenarios, processes in accordance with various embodiments candetermine to capture training data samples to identify a user's trueinteractions in order to identify a correct answer. For example,processes in accordance with a number of embodiments can identify thenext speaker that the user interacts with as the “correct” answer andpair that correct answer with the RSSI values during that low-confidencescenario to form a new training data point.

Processes in accordance with many embodiments can determine whether tocapture a training data sample based on a user's actual (and/orpredicted) interactions (e.g., initiating playback, changing thecurrently playing content, voice commands, physical interactions, etc.)with a device in an MPS. In certain embodiments, training data samplesare captured when a predicted user interaction does not match with theuser's actual interaction. For example, the prediction model may predict“living room speaker,” but the speaker that a user actually controlsusing the Sonos app is “bedroom.”

When the process determines (1410) not to capture training data, theprocess returns to step 1405 and continues to monitor the MPS. When theprocess determines (1410) to capture training data, the process gathers(1415) context data. In numerous embodiments, context data can provideinsight to other factors that may affect a user's interactions beyondthe location of the user within the MPS. Context data in accordance withseveral embodiments can include (but is not limited to) location data,time data, user data, and/or system data. Examples of such data caninclude a probability matrix of nearest devices, time of day, day ofweek, user ID, user preferences, device configuration, currently playingcontent, etc.

Process 1400 identifies (1420) a true user interaction. Userinteractions can be used as a source of ground truth for a machinelearning prediction model. In a variety of embodiments, userinteractions can include interactions with a SONOS device (e.g., tapsone or more of the physical buttons on a SONOS player), voiceinteractions with a SONOS device (e.g., a SONOS player that responds toa voice command), and/or responses to system notifications (e.g., apop-up via the Sonos app saying “What speaker are you closest to?”).User responses in accordance with certain embodiments can include (butare not limited to) selecting a target speaker for playing content froma GUI of a controller device, stopping all playback in the MPS, etc.True user interactions in accordance with numerous embodiments caninclude the non-performance of a predicted interaction (e.g., when aprediction model predicts that a user will initiate playback on akitchen speaker, but the user takes no action at all).

Process 1400 identifies (1422) a predicted user interaction using aprediction model. Prediction models in accordance with numerousembodiments can be trained to take in context data and to predict adesired user interaction. In a variety of embodiments, context dataincludes a probability matrix that is computed as described in examplesabove. In several embodiments, prediction models are pre-trained basedon general data and can then be refined based on local data specific toa user or household. Prediction models in accordance with a variety ofembodiments can be used to predict various elements of a userinteraction, including (but not limited to) a target playback device tobe used, specific content to be played on a playback device, modifyingthe volume of playback, transferring playback between playback devices,etc.

Process 1400 generates (1425) training data based on the true userinteraction and the predicted user interaction. Training data inaccordance with several embodiments can include (but is not limited to)the gathered context data labeled with the true user interaction and/orsome aspect thereof (e.g., a target device). In certain embodiments,training data can be used in an online training process to update theprediction model based on the new training data samples.

Process 1400 updates (1425) the prediction model based on the generatedtraining data. Updating the prediction model in accordance with manyembodiments can include updating weights and/or other parameters of theprediction model based on its ability to predict the true userinteraction. In various embodiments, the prediction model is onlyupdated after a threshold number of training data samples have beengathered. In various embodiments, when a new device is added to an MPSand has no usage information, processes can update the prediction modelwith the new device and provide an initialization probability for thenew device. Initialization probabilities in accordance with manyembodiments can be static initial values to ensure that new devices areat least initially available to a user. In certain embodiments,initialization probabilities can be based on the location of the newdevice (e.g., the initialization probabilities for a new device in theliving room can be initiated with probabilities similar to those ofother nearby speakers). As additional training data samples aregathered, the initialization values can be adjusted based on actualusage.

While specific processes for predicting user interactions are describedabove, any of a variety of processes can be utilized as appropriate tothe requirements of specific applications. In certain embodiments, stepsmay be executed or performed in any order or sequence not limited to theorder and sequence shown and described. In a number of embodiments, someof the above steps may be executed or performed substantiallysimultaneously where appropriate or in parallel to reduce latency andprocessing times. In some embodiments, one or more of the above stepsmay be omitted.

a. Prediction Element

FIG. 15 illustrates an example of a prediction element that trains andpredicts target devices in accordance with an embodiment. Predictionelements in accordance with various embodiments can include (but are notlimited to) controller devices, playback devices, and/or server systems.In this example, prediction element 1500 includes processor 1505,peripherals 1510, network interface 1515, and memory 1520. One skilledin the art will recognize that a particular localization element mayinclude other components that are omitted for brevity without departingfrom the scope of the present disclosure.

The processor 1505 can include (but is not limited to) a processor,microprocessor, controller, or a combination of processors,microprocessor, and/or controllers that performs instructions stored inthe memory 1520 to manipulate data stored in the memory. Processorinstructions can configure the processor 1505 to perform processes inaccordance with certain embodiments.

Peripherals 1510 can include any of a variety of components forcapturing data, such as (but not limited to) cameras, displays, and/orsensors. In a variety of embodiments, peripherals can be used to gatherinputs and/or provide outputs. Network interface 1515 allows predictionelement 1500 to transmit and receive data over a network based upon theinstructions performed by processor 1505. Peripherals and/or networkinterfaces in accordance with many embodiments can be used to gatherinputs (e.g., signals, user inputs, and/or context information) that canbe used to predict user interactions.

Memory 1520 includes a prediction application 1525, training data 1530,and model data 1535. Prediction applications in accordance with severalembodiments can be used to predict user interactions (e.g., targetdevices, desired control actions, etc.) in a networked system ofdevices. In numerous embodiments, training data can include datagenerated at the prediction element based on true and/or predicted userinteractions. Model data in accordance with some embodiments can includeparameters for a machine learning model trained to generateprobabilistic location information based on input signalcharacteristics.

Various parts of described systems and methods for predicting and/ortraining prediction models described herein can be performed on anetworked device and/or remotely, for example, on remote computingdevice(s). In at least some embodiments, any or all of the parts of themethods described herein can be performed on networked device ratherthan at remote computing devices.

Although a specific example of a prediction element 1500 is illustratedin FIG. 15, any of a variety of such elements can be utilized to performprocesses similar to those described herein as appropriate to therequirements of specific applications in accordance with embodiments.

b. Prediction Application

FIG. 16 illustrates an example of a prediction application in accordancewith an embodiment. Prediction applications in accordance with manyembodiments can be used to predict user interactions based on contextdata, including (but not limited to) localization information, userdata, system state data, etc. Prediction application 1600 includescontext engine 1605, prediction engine 1610, training data generator1615, training engine 1620, and output engine 1625.

Context engines in accordance with various embodiments can gathercontext information that can be used to predict a user's interactions.In some embodiments, context engines can perform localization processesfor a portable device to generate a probability matrix that can be usedas an input to a prediction engine. In a variety of embodiments, contextinformation can include identity information for different users (ordevices) in a home, allowing the prediction model to customize thepredictions for each user. Context engines in accordance with variousembodiments can determine system states (e.g., which devices are playingcontent, what content is being played, which devices have been usedrecently, etc.). In certain embodiments, context engines can provide thecontext to a prediction engine to predict a user's interactions.

In several embodiments, prediction engines can take context informationand predict user interactions based on the context information. Userinteractions in accordance with a variety of embodiments can include(but are not limited to) physical interactions, voice interactions,and/or selections of a target device (e.g., through a GUI of acontroller). In a number of embodiments, prediction engines can includea machine learning model such as (but not limited to) neural networks,adversarial networks, and/or logistic regression models that can betrained to predict (or classify) a user interaction based on a givencontext.

Output engines in accordance with numerous embodiments can providevarious outputs based on predicted user interactions from a predictionengine. In certain embodiments, output engines can provide a list oftarget devices, sorted by a likelihood of being the desired targetdevice for a user interaction, which can be displayed at a controllerdevice to allow a user to have quick access to controls for the mostlikely target devices. Output engines in accordance with a number ofembodiments can transmit control instructions (e.g., to initiateplayback, stop playback, modify volume, etc.) to playback devices in anMPS.

Training data generators in accordance with some embodiments cangenerate training data based on a user's interactions with devices in asystem. In many embodiments, training data generators can performprocesses similar to those of FIG. 14 to generate training data samplesthat include context data and true user interactions.

In many embodiments, training engines can train prediction engines in anonline fashion using training data generated based on user interactionswith an MPS. In a variety of embodiments, training engines can computelosses based on a difference between a predicted user interaction and anactual user interaction. For example, when a prediction engine predictsa user's actual interaction with a confidence level of 0.6, the loss canbe computed as 0.4. Computed losses can be used to update parameters fora model (e.g., through backpropagation to update weights of a neuralnetwork).

Although a specific example of a prediction application 1600 isillustrated in FIG. 16, any of a variety of localization applicationscan be utilized to perform processes for localizing portable devicessimilar to those described herein as appropriate to the requirements ofspecific applications in accordance with embodiments. In someembodiments, one or more of the above elements may be omitted and/oradditional elements may be added.

c. Example Applications

Predicting target devices can have various applications in an MPS, suchas (but not limited to) automated playback of content at a targetdevice, ranked and/or filtered lists of target devices, etc. Examples ofa graphical user interface (GUI) based on predicted target devices areillustrated in FIGS. 17A-B. FIG. 17A illustrates GUI 1700A in two stages1710 and 1720. The first stage 1710 shows GUI 1700A with controlelements (e.g., control element 1712 for the Living Room and controlelement 1714 for the Kitchen) for controlling playback at differentareas of a home. The second stage 1720 shows GUI 1700A after a targetdevice prediction process. In this example, the target device predictionprocess has predicted (e.g., based on the time of day, user's relativelocation, etc.) that the Kitchen is more likely to be the target device(or region) than the Living Room. In the second stage 1720, thepositions of control element 1712 for the Living Room and controlelement 1714 for the Kitchen have been switched, moving the controlelement 1714 for the Kitchen into the top position.

Similarly, FIG. 17B shows GUI 1700B in two stages 1730 and 1740. Thefirst stage 1730 shows GUI 1700B with control elements (e.g., controlelement 1712 for the Living Room) for controlling playback at differentareas of a home. The control element 1712 for the Living Room has beenhighlighted as the most likely to be the target device. The second stage1740 shows GUI 1700B after a target device prediction process, where thetarget device prediction process has predicted (e.g., based on the timeof day, user's relative location, etc.) that the Dining Room is morelikely to be the target device (or region) than the Living Room. In thesecond stage 1740, the positions of control elements do not change, butnow the control element 1716 for the Dining Room is highlighted insteadof the control element 1712 for the Living Room. GUIs in accordance withsome embodiments of the invention can implement various differentapproaches to emphasizing a target device, such as (but not limited to)displaying a limited (e.g., the top n) list of target devices, reducingthe contrast of non-target devices, displaying target devices in adifferent color and/or size, placement of controls for predicted targetdevices within the display, etc.

Although specific examples of a GUI are illustrated in FIGS. 17A-B, anyof a variety of such GUIs can be utilized to perform processes similarto those described herein as appropriate to the requirements of specificapplications in accordance with embodiments.

V. Conclusion

The description above discloses, among other things, various examplesystems, methods, apparatus, and articles of manufacture including,among other components, firmware and/or software executed on hardware.It is understood that such examples are merely illustrative and shouldnot be considered as limiting. For example, it is contemplated that anyor all of the firmware, hardware, and/or software aspects or componentscan be embodied exclusively in hardware, exclusively in software,exclusively in firmware, or in any combination of hardware, software,and/or firmware. Accordingly, the examples provided are not the onlyway(s) to implement such systems, methods, apparatus, and/or articles ofmanufacture.

In addition to the examples described herein with respect to stationaryplayback devices, embodiments of the present technology can be appliedto headphones, earbuds, or other in-or over-ear playback devices. Forexample, such in- or over-ear playback devices can includenoise-cancellation functionality to reduce the user's perception ofoutside noise during playback. In some embodiments, noise classificationcan be used to modulate noise cancellation under certain conditions. Forexample, if a user is listening to music with noise-cancellingheadphones, the noise cancellation feature may be temporarily disabledor down-regulated when a user's doorbell rings. Alternatively oradditionally, the playback volume may be adjusted based on detection ofthe doorbell chime. By detecting the sound of the doorbell (e.g., bycorrectly classifying the doorbell based on received sound metadata),the noise cancellation functionality can be modified so that the user isable to hear the doorbell even while wearing noise-cancellingheadphones. Various other approaches can be used to modulate performanceparameters of headphones or other such devices based on the noiseclassification techniques described herein.

The specification is presented largely in terms of illustrativeenvironments, systems, procedures, steps, logic blocks, processing, andother symbolic representations that directly or indirectly resemble theoperations of data processing devices coupled to networks. These processdescriptions and representations are typically used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art. Numerous specific details are set forth to provide athorough understanding of the present disclosure. However, it isunderstood to those skilled in the art that certain embodiments of thepresent disclosure can be practiced without certain, specific details.In other instances, well known methods, procedures, components, andcircuitry have not been described in detail to avoid unnecessarilyobscuring aspects of the embodiments. Accordingly, the scope of thepresent disclosure is defined by the appended claims rather than theforgoing description of embodiments.

When any of the appended claims are read to cover a purely softwareand/or firmware implementation, at least one of the elements in at leastone example is hereby expressly defined to include a tangible,non-transitory medium such as a memory, DVD, CD, Blu-ray, and so on,storing the software and/or firmware.

1. A method for identifying a target reference playback device in amedia playback system comprising a plurality of reference devices, themethod comprising: measuring characteristics of signals transmitted viasignal paths between each of the plurality of reference devices over aperiod of time, wherein the plurality of reference devices comprises aplurality of reference playback devices; measuring characteristics ofsignals transmitted via signal paths between the portable device andeach of the plurality of reference devices; normalizing the measuredcharacteristics to estimate characteristics of the signal paths betweeneach of the plurality of reference devices and between the portabledevice and each of the plurality of reference devices based on anaggregate statistic of at least one characteristic for a first set ofone or more signals from a first reference device to a second referencedevice and a second set of one or more signals from the second referencedevice to the first reference device; identifying a particular locationassociated with the portable device using the normalized characteristicsof the signal paths between each of the plurality of reference devicesand the normalized characteristics of the signal paths between theportable device and each of the plurality of reference devices; andidentifying a set of one or more target reference playback device of theplurality of reference devices based on the identified particularlocation.
 2. The method of claim 1, wherein at least two of theplurality of reference devices include different transmitterimplementations.
 3. The method of claim 1, wherein the portable devicehas a transmitter implementation that differs from the transmitterimplementations of at least one of the reference devices.
 4. The methodof claim 1, wherein the measured characteristics of a particular signalcomprise at least one of a received signal strength indicator (RSSI)value, an identifier for a sending reference device that transmitted theparticular signal, and a timestamp for the particular signal.
 5. Themethod of claim 1, wherein identifying the particular location comprisesestimating a likelihood that the portable device is in a particularlocation by computing a set of probabilities that the portable device isnear each of at least one reference device of the plurality of referencedevices.
 6. The method of claim 5, wherein normalizing the measurementsfor a first reference device comprises: calculating a firstsignal-strength ratio of signals received at the first reference devicefrom the portable device to signals received at the first referencedevice from a second reference device; and calculating a secondsignal-strength ratio of signals received at the second reference devicefrom the portable device to signals received at the second referencedevice from the first reference device; wherein computing the set ofprobabilities for the first reference device comprises computing a ratioof the first signal-strength ratio to the second signal-strength ratio.7. The method of claim 5, wherein normalizing the measurements for afirst reference device comprises: calculating a first signal-strengthratio of signals received at the first reference device from theportable device to signals received at the first reference device from asecond reference device; and calculating a second signal-strength ratioof signals received at a third reference device from the portable deviceto signals received at the third reference device from the secondreference device; wherein computing the set of probabilities for thefirst reference device comprises computing a ratio of the firstsignal-strength ratio to the second signal-strength ratio.
 8. The methodof claim 5, wherein computing the set of probabilities comprises:identifying an offset based on a difference in RSSI values for the firstsignal path and the second signal path; and determining a normalized setof one or more RSSI values for the first and second signal paths basedon the identified offset.
 9. The method of claim 1, wherein theaggregate statistic is a weighted average based on timestamps associatedwith the first and second sets of signals.
 10. The method of claim 1further comprising estimating which of the reference devices is closestto the portable device based on the identified particular location. 11.The method of claim 1 further comprising selecting a computing referencedevice of the plurality of reference devices for performing the steps ofnormalizing the measurements and identifying the particular location.12. The method of claim 11, wherein selecting the computing referencedevice comprises identifying an idle reference device of the pluralityof reference devices.
 13. The method of claim 11, wherein selecting thecomputing reference device comprises identifying an infrequently-usedreference device of the plurality of reference devices.
 14. The methodof claim 1 further comprising: identifying a nearest reference devicefrom the set of reference devices based on the identified particularlocation; and transferring audio playing at the portable device to playat the nearest reference device.
 15. The method of claim 1, whereinidentifying a particular location comprises estimating a likelihood thatthe portable device is in a particular location, wherein the methodfurther comprises determining a change in location based on changes inthe estimated likelihood over a duration of time.
 16. The method ofclaim 1, wherein the plurality of reference devices further comprises aset of one or more controller devices for controlling playback devicesin the media playback system.
 17. A playback device comprising: one ormore amplifiers configured to drive one or more speakers; at least oneprocessor; at least one non-transitory computer-readable mediumcomprising program instructions that are executable by the at least oneprocessor such that the playback device is configured to: obtaincharacteristics of signals transmitted via signal paths between each ofa plurality of reference playback devices in a media playback systemover a period of time; obtain characteristics of signals transmitted viasignal paths between a portable device and each of the plurality ofreference playback devices; normalize the obtained characteristics toestimate characteristics of the signal paths between each of theplurality of reference playback devices and between the portable deviceand each of the reference playback devices based on an aggregatestatistic of at least one characteristic for a first set of one or moresignals from the first reference device to the second reference deviceand a second set of one or more signals from the second reference deviceto the first reference device; identify a particular location associatedwith the portable device using the normalized characteristics of thesignal paths between each of the plurality of reference playback devicesand the normalized characteristics of the signal paths between theportable device and each of the plurality of reference playback devices;identify a ranked listing of a plurality of target reference playbackdevices of the plurality of reference playback devices based on theidentified particular location; and transmit the ranked listing oftarget reference playback devices to the portable device, wherein theportable device modifies a user interface at the portable device basedon the ranked listing.
 18. The playback device of claim 17, wherein theplayback device is one of the plurality of reference playback devices.19. A controller device, the controller device comprising: a displayconfigured to display a graphical user interface; at least oneprocessor; and at least one non-transitory computer-readable mediumcomprising program instructions that are executable by the at least oneprocessor such that the controller device is configured to: obtaincharacteristics of signals transmitted via signal paths between each ofa plurality of reference playback devices in a media playback systemover a period of time; obtain characteristics of signals transmitted viasignal paths between the controller device and each of the plurality ofreference playback devices; normalize the obtained characteristics toestimate characteristics of the signal paths between each of theplurality of reference playback devices and between the controllerdevice and each of the reference playback devices based on an aggregatestatistic of at least one characteristic for a first plurality signalsfrom the first reference device to the second reference device and asecond plurality of signals from the second reference device to thefirst reference device; identify a particular location associated withthe controller device using the normalized characteristics of the signalpaths between each of the plurality of reference playback devices andthe normalized characteristics of the signal paths between thecontroller device and each of the plurality of reference playbackdevices; identify a set of one or more target reference playback devicesof the plurality of reference playback devices based on the identifiedparticular location; and modify the graphical user interface displayedon the display based on the identified set of target reference playbackdevices.